CN1610837A - Method for detecting location of mobile terminal - Google Patents

Abstract

An object of the invention is to determine the location of a mobile terminal as a maximum likelihood estimate value, in a mobile communications system comprising a plurality of base stations, at least one mobile terminal capable of communicating with the base stations, and a center station capable of communicating with the base stations and/or mobile terminal. The probability distribution indicating the variation in the reception level from respective base stations CSi at a plurality of points within a prescribed range in which the mobile terminal has a possibility of being present is determined for these points on the basis of a base station database storing, at the least, positional information for the respective base stations CSi. A likelihood corresponding to the aforementioned probability distribution is determined with respect to the reception level values [gamma]o from the base stations CSi (i = 1 to N) measured by the mobile station, and the point having the greatest likelihood value is determined to be the estimated location of the mobile terminal.

Description

Method for detecting location of mobile terminal

technical field

The present invention relates to a position detection technique for a mobile terminal such as a mobile wireless terminal, and more particularly to a position detection technique for detecting the position of a mobile terminal in consideration of a probability change in a signal reception level.

Background technique

In the prior art, for a mobile communication system including a plurality of base stations, at least one mobile terminal capable of transmitting and receiving signals with the base stations, and a central station (control station) capable of communicating with the base stations and/or mobile stations, an identification A technology to (detect) the location of a mobile terminal.

For example, there is a method in which radio waves transmitted by a mobile terminal (such as a portable phone) are received by base stations, the distance from the mobile terminal to the base station is recognized based on the difference in the intensity and/or arrival time of radio waves received by each base station, and the The distance between the terminal and the plurality of base stations identifies the location of the mobile terminal.

Furthermore, conversely there is also a method in which radio waves transmitted by a plurality of base stations are received by one mobile terminal, and the distance from the mobile terminal to the base station is recognized based on the difference in the strength and/or arrival time of the radio waves from the respective base stations , and identify the location of the mobile terminal according to the distance between the mobile terminal and multiple base stations.

More specifically, known examples of such methods include position recognition methods based on a curve intersection method, an SX method (sphere intersection method), a PX method (plane intersection method), and the like. These different techniques are known to those skilled in the art, and their details are in (1) Ralph O. Schmidt, "A New Approach to Geometry of Range Difference Location", IEEE Transactions on Aerospace and Electronic Systems, Vol. AES- 8, No. 6, November 1972, and (2) Julius O. Smith, Jonathan S. Abel, "Closed-Form Least-Squares Source Location Estimation trom Range-Difference Measurement", IEEE Transactions on Acoustics, Speech, and Signal Processing, Described in VOL.ASSP-35, No.12, December 1987.

Contents of the invention

These general location recognition methods include the following issues. For example, in the curve intersection method, since a nonlinear equation must be solved, the computational processing load is very large. On the other hand, in the SX method or the PX method, solutions are derived from linear equations, so they do not deal with this problem of curve intersection methods, but these methods are greatly affected by errors in observations, etc., so it is difficult to identify location with sufficient precision.

There are also the following problems. The reception of radio waves received by a mobile terminal from a base station according to a combination of long section variation due to distance attenuation, short section variation due to construction, and instantaneous variation due to movement within an electric field Levels vary widely. Here, assuming that the short-section variation follows a predetermined probability distribution, ideally the position of the mobile terminal should be identified according to the maximum likelihood estimation method. However, in the general approach described above, the short-section variation is not considered, or if it is, is taken only as measurement error, with the result that in either case the determined position of the mobile terminal does not represent a maximum likelihood estimate.

It is therefore an object of the present invention to determine the mobile terminal position as a maximum likelihood estimate, taking into account the trade-off between computational processing burden and estimation accuracy.

The position detection method of the present invention is a position detection method for a mobile communication system composed of a plurality of base stations, a mobile terminal and a control station, wherein the base stations send signals, and the signals from the base stations include a unique number of each base station, The mobile terminal receives signals from one or more of the N base stations, measures and stores the received level Γi from the i-th base station, and transmits the first to Nth received levels to the control station via the communication base station. At the control station, according to Identify the mobile terminal based on the database created based on the base station number, the base station coordinates, and the reception level of radio wave propagation previously obtained by measurement or simulation or measurement and simulation at various points within the service area of each base station, and the reception level transmitted by the mobile terminal Position, it is characterized in that, it comprises the steps: set two-dimensional or three-dimensional mesh shape or grid shape coordinate point in the whole service area of each base station; ”), determine the estimated value of the average reception level at each candidate point from the coordinate point, and use the estimated value as an average value to determine the probability density function of the change in the estimated value; according to the measured reception level, at In a given range, each signal receiving base station integrates the probability density function determined for the candidate point at each candidate point; the likelihood of the candidate point is determined by multiplying the values obtained by integrating each signal receiving base station at each candidate point and detecting the candidate point with the maximum likelihood value as the estimated position of the mobile terminal.

In addition, the method of the present invention further includes the step of: corresponding to each base station receiving the signal (hereinafter referred to as "signal receiving base station"), determining the estimated value of the average receiving level at each candidate point from the coordinate point, and This estimated value is used as the average value, and the probability density function of the average value change is determined; according to the receiving level measured by each signal receiving base station at each candidate point, the measured receiving level is substituted into the probability density function determined for each candidate point ; determining the likelihood of the candidate point by multiplying the values obtained by substituting each signal receiving base station at each candidate point; and detecting the candidate point with the maximum likelihood value as the estimated position of the mobile terminal.

Furthermore, the method of the present invention also includes the step of: corresponding to each base station receiving the signal (hereinafter referred to as "signal receiving base station"), determining the estimated value of the average receiving level at each candidate point from the coordinate point, and Using this estimated value as the average value, determine the probability density function of the average value change; according to the receiving level measured by each signal receiving base station at each candidate point, the measurement data is substituted into the probability density function determined for each candidate point, and multiplied by a predetermined value; determining the likelihood of the candidate point by multiplying the values obtained by substituting each signal receiving base station at each candidate point; and detecting the candidate point having the maximum likelihood value as a mobile terminal the estimated location of .

Preferably, the likelihood can be calculated by separately estimating parameters related to the probability density function and the propagation characteristic equation for each corresponding base station.

Preferably, the likelihood can be calculated by separately estimating parameters related to the probability density function and the propagation characteristic equation for each cell or each grid point.

Preferably, the likelihood can be calculated by using propagation characteristic equations with different probability density functions according to time of day, day of week, season of year, traffic density, etc., in these cases the desired propagation characteristics take into account buildings, Terrain conditions etc.

Preferably, in the case where the base station has sector components, the coordinates and the direction angles of the sectors are linked together, and a joint probability is set, wherein the difference between the direction angles of each sector is used as a factor, by combining the joint probability and the corresponding The likelihoods of the coordinates are multiplied to determine the likelihoods.

Preferably, the long-section propagation estimation equation and the short-section average probability density are determined from the propagation characteristics considering coordinates and building or terrain conditions between base stations according to building information or map information.

Preferably, a plurality of estimated positions can be set, in other words, an estimated region in which the likelihood is greater than a certain specified value can be set.

Preferably, propagation delay times may be used instead of reception levels.

The method for creating a database according to the present invention is a method for creating a database for detecting the position of a mobile terminal in a mobile communication system. The mobile communication system is composed of a plurality of base stations, mobile terminals and a control station, and is characterized in that it includes Steps: store base station numbers, base station coordinates, and radio wave propagation reception levels previously obtained through measurement or simulation, or through measurement and simulation at various points in the service area of each base station, as data in an interrelated manner; according to the above-mentioned stored data, Detecting a characteristic point, comparing the received level of other nearby points, the measured reception level of which is unique; and storing a propagation characteristic parameter corresponding to the detected characteristic point.

Furthermore, the method of creating a database according to the present invention is a method of creating a database for detecting the position of a mobile terminal in a mobile communication system consisting of a plurality of base stations, mobile terminals, and a control station, wherein, to determine is the standard deviation of one of the parameters of the probability density function, using and measuring multiple receive antennas simultaneously, creating a database by identifying the measurement locations on the order of a few meters.

The position recognition method according to the present invention is a kind of for mobile communication system, according to the CSi (i=1-N, N≥1) signal reception level value Γk(CSi) from the base station measured at the mobile terminal and stored with at least The base station database of the position information of the base station CSi, to determine the position identification method of the estimated position of the mobile terminal, the mobile communication system includes a plurality of base stations, at least one mobile terminal capable of communicating with the base station, and a center capable of communicating with the base station and/or mobile terminal station, wherein the likelihoods of the reception level value Γk(CSi) are determined for a plurality of points based on the probability distribution indicating the change in the reception level of the signal from the base station CSi at the point, and the point with the maximum likelihood value is determined as The estimated location of the mobile terminal.

The position identification method according to the present invention is a mobile communication system, according to the signal receiving level value Γk(CSi) from the base station CSi (i=1-N, N≥1) measured at the mobile terminal and stored at least The base station database of the position information of the base station CSi is used to determine the position identification method for the estimated position of the mobile terminal. The mobile communication system includes a plurality of base stations, at least one mobile terminal capable of communicating with the base station, and a mobile terminal capable of communicating with the base station and/or mobile terminal Central station, the method includes: the first step of setting the approximate location range of the mobile terminal, and the second step of setting a plurality of representative points Xs (s=1-M) within the approximate location range; The distance d(Xs, CSi) between the representative point Xs and the base station CSi, the third step of determining the probability density function Ps(γ _i ) representing the change in the reception level from the base station CSi at the representative point Xs; through the equation jp _s (γ)=Ps(γ ₁ )×Ps(γ ₂ )×……×Ps(γ _N ) The fourth step of determining the joint probability density function jp _s (γ); determining the corresponding joint probability density function The fifth step of the probability distribution likelihood Ls(jp _s (γ)|Гk) specified by jp s (γ); and determining the representative point Xs ^* with _the maximum likelihood value Ls(jp _s (γ)|Гk) as The sixth step for the mobile terminal to estimate the position.

The position identification system according to the present invention is a mobile terminal position identification system for identifying the position of a mobile terminal capable of communicating with a plurality of base stations, and includes a central station which has information from base stations CSi (i=1-N , N≥1) signal reception level value Γk (CSi) to determine the function of the estimated position of the mobile terminal, also has a base station database that stores at least the position information of the base station CSi, and the central station also includes a position for implementing the position according to the present invention The device or function of the identification method.

The program according to the present invention is characterized in that it enables the individual steps of the position detection method of the present invention to be executed in a computer. The program according to the present invention can be loaded or loaded into a computer through various types of storage media such as CD-ROM, magnetic disk, semiconductor memory, and the like.

In addition, in the present description, "apparatus" not only means a physical device, but also includes a case where the functions provided by the apparatus are realized by software. Also, the functions of one device can be realized by two or more physical devices, and the functions of two or more devices can be realized by one physical device.

Brief description of the drawings

FIG. 1 is a diagram showing the structure of a mobile communication system in a first embodiment of the present invention;

Fig. 2 is a block diagram showing the functional structure of the central station;

FIG. 3 is a diagram showing a data structure of a base station database;

Fig. 4 is a flow chart describing the processing sequence of the position recognition device;

FIG. 5 is a diagram describing a data structure of a base station database in a modified example;

FIG. 6 is a diagram describing a data structure of a base station database in a modified example;

Figure 7 is a diagram describing the location configuration of base stations in the experiment;

Figure 8 is a graph depicting simulation results;

Figure 9 is a graph depicting simulation results;

Figure 10 is a graph depicting simulation results;

FIG. 11 is a graph depicting simulation results;

FIG. 12 is a graph describing results obtained from measurement data;

FIG. 13 is a graph describing results obtained from measurement data;

Fig. 14 is a diagram describing an example of a characteristic point;

Fig. 15 is a diagram describing an example of a characteristic point;

Fig. 16 is a diagram describing an example of a characteristic point;

Fig. 17 is a diagram describing an example of a characteristic point;

Fig. 18 is a diagram describing an example of a characteristic point;

Fig. 19 is a diagram describing an example of a characteristic point;

Fig. 20 is a diagram describing an example of a characteristic point; and

Fig. 21 is a diagram describing an example of a characteristic point.

Detailed ways

(first embodiment)

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 1 is a conceptual diagram showing the composition of a mobile communication system forming a first embodiment of the present invention. The system consists of a mobile terminal 10, a plurality of base stations CS to communicate with the mobile terminal 10, and a central station (control station) 90 to communicate with the base stations CS.

Generally, the mobile terminal 10 and the base station CS have the same functional components of a general mobile communication system composed of PHS (portable telephone or the like). For example, when transmitting a signal, the base station CS has a function of including an identification ID (identification information) unique to the base station in a wireless signal, or other similar functions. In addition, the mobile terminal 10 has a function of receiving radio waves from the base station CSi (where i=1-N; N is the number of base stations capable of communicating with the mobile terminal 10; N≥1), and can measure and store its received power. Flat Гo (Г1o, Г2o, ... Г _N o) (dB), etc.

The base station CS includes the mobile terminal 10 within the control area (service area; hereinafter referred to as "area or cell"), or the base station CS communicating with the mobile terminal 10, receives the above-mentioned information from the 1st to the Nth base station from the mobile terminal 10. receive level Гi0 and send it to the central station 90. It is also possible to employ a structure in which the reception level Γio is directly sent from the mobile terminal 10 to the center station 90 without passing through the base station.

FIG. 2 shows the functional components of the central station 90 . As shown in FIG. 2 , the central station 90 includes at least a base station database 91 for storing location information of the base station CS, and a location identification device 92 for identifying the location of the mobile terminal 10 and thus providing it with a location identification system function.

Although not shown in FIG. 2, the center station 90 includes, in addition to the above, standard functional components provided in the center station of the universal mobile communication system (for example, functions of transmitting signals to and receiving signals from base stations and mobile terminals, The function of displaying the location of a specific mobile terminal, the function of sending signals to and receiving signals from users using location information, etc.).

Physically, the central station 90 may be composed of a dedicated station, or may be composed of a general information processing device. For example, the central station 90 can operate and specify different processes (such as respective functions, and the position recognition method according to the present invention, etc.) software implementation.

The base station database 91 stores at least the location information (such as latitude and longitude) of the base station CSi which is consistent with the base station identification ID, and the base station database of the universal mobile communication system can also be used for the same purpose. The database also stores base station addresses, outputs (effective radiated power), antenna heights, propagation characteristics (propagation characteristic parameters α described here and below, etc.). Figure 3(a) shows an example of the data structure of the base station database.

The location identifying means 92 identifies the location of the mobile terminal based on the radio wave reception level Γio from the base station CSi measured by the mobile terminal 10 and the base station database 91 . Next, the processing procedure performed by the position recognition device 92 will be described based on the flowchart of FIG. 4 . The individual steps may be performed in any different order as long as no inconsistencies arise in the processes performed.

(first example)

In step S100 , an approximate location range of the mobile terminal 10 is set. The approximate location range indicates a large area where the mobile terminal 10 may appear, and the approximate location range can be set, for example, as a location registration area (general paging area) of the mobile terminal 10, or a base station managed by a base station establishing a communication circuit with the mobile terminal 10. area or district. In addition, the approximate location range can also be set around base stations whose reception levels are at or above a specified value.

The approximate position range can also be set as a three-dimensional area, not just a two-dimensional area. In addition, the method of setting the approximate position range is not limited to the above, and the shape, width, etc. of the approximate position range can be determined according to the system design.

In step S101, a plurality of assumed positions Xs are set for the mobile terminal within the approximate position range mentioned above (where s=1-M; M is the number of assumed positions). The assumed location is a point that forms a reference for identifying the location of the mobile terminal 10 . A method of setting an assumed position includes, for example, dividing an approximate position range into a two-dimensional or three-dimensional mesh (grid), and setting an assumed position at each grid point. The cell size can be set according to the system design (depending on the trade-off between position recognition accuracy and computational processing burden), and it can be understood that it can be set to 5-meter units, 10-meter units, or 100-meter units.

The assumed position can be set by previously dividing the entire area or cell of each base station into a two-dimensional or three-dimensional cell shape (mesh shape). In this example, in step S101, a plurality of presumed positions Xs previously set within an approximate position range are selected.

Therefore, the likelihood calculation processing in steps S102 to S106 is performed for the above-mentioned respective set assumed positions Xs.

In step S102, a plurality of base stations CSj are selected from the base stations CSi, and the distance (Xs, CSj) between the base stations CSj and the assumed position Xs is calculated with reference to the base station database 91 . Here, the way to select the base station CSj can be determined according to the system design, for example, select a specified number (eg about 1 to 10) of base stations from the base stations CSi in the order of the highest reception level Γio. On the other hand, it is also possible to employ a combination in which all base stations CSi for which the reception level Γio is measured are selected. In general, the more base stations selected, the higher the accuracy of the identified position. In the following description, it is assumed that all base stations CSi are selected.

In step S103, the average reception level Γim(Xs, CSi) from the base station CSi at the assumed position Xs is calculated from the distance d(Xs, CSi) between the base station CSi and the assumed position Xs. The average reception level Гim(Xs, CSi) can be calculated according to the propagation characteristic equation (1) below, assuming that the long-section variation follows the Okumura curve.

Г _im (Xs, CSi) = A _i -10α _i ×log(d(Xs, CSi))[dB] (1)

Here, A _i is the reception level when the distance is a unit distance (for example, 1 meter), and α _i is the antenna coefficient.

According to the effective radiation power stored in the base station database 91, the antenna height, etc., the parameter A _i can be determined for each base station respectively. More precisely, the parameter A _i may be determined according to the antenna sensitivity of the mobile terminal. It is also possible to determine the parameter A _i in advance and store it in the base station database as propagation characteristic information, as shown in Fig. 3(a) and the like.

Based on the radio wave reflection, shielding, and diffraction (hereinafter referred to as "reflection, etc.") values obtained by simulation based on the surrounding conditions of the base station by referring to the three-dimensional map information, or based on the measured value, or based on the simulated value and the measured value, each base station is independently pre-prepared. Determine the parameter α _i . Desirably, the predetermined value α _i is stored in the base station database as propagation characteristic information, as shown in Fig. 3(a).

Desirably, the parameters A _i and α _i are determined separately for each base station, but combinations using common parameter values A, α for all base stations may also be employed. In addition, an equation other than the Okumura curve may also be used as the propagation characteristic equation, and in such a case, parameters are determined according to the employed equation.

In step S104, the probability density function P _xs (Γ _i ) indicates the short-section variation of the reception level from the base station CSi at the assumed position Xs (in other words, takes the assumed position Xs as a parameter). P _xs (Γ _i ) can be calculated from the following equation (2).

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; xs</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \{</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; im</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; Xs</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; CSi</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; \}</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Here, as shown by equation (2), in the current embodiment, the short-section variation follows a decibel-normal distribution. The element σ in equation (2) corresponds to the standard deviation of the decibel-normal distribution, and this value is determined according to the system design, and 4-6dB can be selected.

Equation (2) can be formulated by assuming that the short-section variation follows another probability distribution.

In step S105, the joint probability density function jp _xs (Γ) at the assumed position Xs is determined according to the following equation (3), assuming that the variation of the reception level from each base station is independent,

jp _xs (Г) = P _xs (Г ₁ )×P _xs (Г ₂ )×......×P _XS (Γ _N ) (3)

_{In step} S106 _, the likelihood value _{L xs} ( Г _1o , Г _2o , ... Г _No ). The likelihood value L _xs (Γ _1o , Γ _2o , . . . Γ _No ) can be calculated using the differential value ΔΓ according to the following equation (4).

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; xs</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;\&Gamma;</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Delta;\&Gamma;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d\&Gamma;</span>}<span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; \&times;</span>\underset{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;\&Gamma;</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Delta;\&Gamma;</span>}}{<span\; class="notranslate">\; \&Integral;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d\&Gamma;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

Here, in Equation (4), the elements P ₁ , . . . , P _N on the right side correspond to the received signals from the base stations CS ₁ , . . . CS _N at the assumed position Xs. Probability density functions P _xs (Г ₁ ), ..., P _xs (Г _N ) of the variation of the flat short section.

Since P _i (Г) is a probability density function, each element on the left side of equation (4) partially forms the integral, which has a value between 0 and 1, so the likelihood value L represented by their multiplication _xs (Г _1o , Г _2o , ... Г _No ) will also have values between 0 and 1. Furthermore, an integral approximation can be obtained by multiplying the probability density value by 2×ΔΓ.

After the likelihoods _Lxs (Γ _1o , Γ _2o , . . . Γ _No ) are calculated for all assumed position groups, the procedure proceeds to step S107.

In step S107, the S value with the maximum likelihood L _xs (Γ _1o , Γ _2o , ... Γ _No ) is selected from S=1-M (hereinafter, the selected S is referred to as S ^* ), and specify Xs ^* to be the estimated position of the mobile terminal.

Since the joint probability density function for each hypothetical position generally does not depend on the measured reception level Γ _i , it can be predetermined.

(amendment 1)

In the first example, the processing is constituted in such a manner that the propagation characteristic equation independently set for each base station is used in step S103 in consideration of the difference in the location conditions of each base station.

However, due to atmospheric conditions, and conditions such as reflection, diffraction, etc., which vary with time of day, day of the week, seasons of the year, etc., it is understood that propagation characteristics vary even at the same base station due to these conditions. The traffic density of vehicles etc. also affects the propagation characteristics. In addition, the presence of buildings and the like between each assumed position and the base station, and terrain conditions and the like between them also affect propagation characteristics.

Therefore, in the first modification, a configuration is adopted in which multiple data related to at least one of time information (such as time of day, day of the week, season, etc.), traffic density, assumed position, etc. are stored in the base station database 91 for each base station. propagation characteristic parameters α _i (and/or parameters A _i ) (see FIG. 5 ). α _i (and/or A can be independently predetermined based on, for example, reference to three-dimensional map information, radio wave reflection values obtained through simulation based on surrounding conditions of the base station, or based on measured values of reception levels, or based on simulated values and measured values. _i ) for each value.

Furthermore, step S103 in the position recognition means 92 is composed as follows. That is, at least time information or traffic information is obtained from an internal device of the center station 90 or from an external device at the time of estimation. By referring to the base station database 91 to read the propagation characteristic parameter α _i (and/or A _i ) corresponding to the information thus obtained, and/or the assumed position Xs, the value of the read α _i (and/or A _i ) is used according to Equation (1) determines Γ _im (Xs, CSi). Other steps are the same as in the first example.

In this way, a composition is obtained in which propagation characteristics can be selected and used according to time information (such as time of day, day of the week, season of the year, etc.), traffic density, assumed position, etc., so that position recognition accuracy can be further improved.

(characteristic point)

Here, when storing the propagation characteristic parameters α _i (and/or parameters A _i ) related to the assumed positions in the base station database 91, desirably, each grid point of the cell that divides the approximate position range as described above In addition, the characteristic points defined below are also selected as the assumed positions.

A characteristic point can be understood as a measurement point because complex radio wave propagation characteristics occur due to influence of conditions such as the shape of a building surrounding the measurement point, and its relative position with respect to the base station, when comparing the received measurement data, in The reception level at this point is significantly different from the measurement data at nearby measurement points.

In the present invention, a structure is employed in which the position is identified by determining the likelihood of the measured reception level from a probability density function indicating a short-section change in the reception level at each assumed position, and thus by selecting a display different from The characteristic points of the radio wave propagation characteristics of the surrounding points and different probability density functions are used as assumed positions, which can reduce position identification errors.

Therefore, in the preferred embodiment of the present invention considering the characteristic point, the base station database 91 is created by a database creation method, which includes the steps of: storing the base station number (base station identification ID), base station coordinates ( The position information of the base station CSi), and the radio wave propagation reception level previously obtained by measurement or simulation or measurement and simulation at each point in the service area of each base station; according to the above-mentioned stored data, a characteristic point is detected, and the reception level is measured at the characteristic point The reception level is significantly different when compared with other nearby point reception levels; and the propagation characteristic parameter α _i (and/or parameter A _i ) corresponding to the detected characteristic point is stored.

For example, the following pattern of characteristic points can be imagined in the case of urban terrain. 1) Near building gaps at prescribed intervals (e.g. 2-3 meters); 2) Near the edge between the building and local open space (open parking lot, empty space left by removal of building, etc.); 3) Before building , which is further back than nearby buildings; 4) located in a low building group affected by high building shielding or reflection; 5) near a low building located in a high building group; 6) near a large building with an irregular shape; 7) points with better radio transparency compared to nearby points (e.g. intersections); 8) buildings constructed of different materials (e.g. steel and wood, near buildings with reflective surface walls, glass-covered and tile-covered structures); wait.

Next, a method of detecting a characteristic point based on a measured reception level in the above mode will be described.

In the first mode, as shown in Fig. 14A, the reception level is measured at points 304, 107, 108. This figure shows the relative position of buildings and roads in line diagram form (applying the same method to Figure 15 below, etc.) when looking down from a particular position on high. At each point, signals may be received from at least three base stations A-150, A-210, A-270. Fig. 14B shows reception levels received from respective base stations at various points. Here, since the points 304, 107 and 108 are close to each other, generally, they show similar tendencies in their reception levels, but actually, the reception level from the base station A-270 at the point 304 is 55.1dBuV/m, which is greater than From the received level at other points the expected value of 45dBuV/m is inferred. This is believed to occur due to the fact that point 304 is located in a gap between buildings, thus the reception level increases according to the improved radio visibility compared to other points. According to the method of creating a database of the present invention, a point 304 is detected as a characteristic point, and propagation characteristic parameters α _i etc. are stored at the point 304 so as to be used as assumed positions.

In the second mode, the reception level is measured at points 308, 175 and 176 shown in Fig. 15A. At each point, signals may be received from at least three base stations A-330, A-30, A-90. Fig. 15B shows reception levels received from respective base stations at respective points. Here, since the points 308, 175 and 176 are close to each other, generally, they show similar tendencies in their reception levels, but actually, the reception level from the base station A-330 at the point 308 is 39.7dBuV/m, less than The received level from other points tends towards the expected inferred value of 55dBuV/m. This is thought to be due to the fact that point 308 is located between the building and open space, and thus is shielded by the building, reducing its reception level.

In the third mode, the reception level is measured at points 306, 127 and 128 shown in Fig. 16A. At each point, signals may be received from at least three base stations A-180, A-240, A-300. Fig. 16B shows reception levels received from respective base stations at various points. Here, since the points 306, 127 and 128 are close to each other, generally, they show similar tendencies in their reception levels, but actually, the reception level from the base station A-300 at the point 306 is 43.4 dBuV/m, a high The received level from other points tends to the expected inferred value of 35-40dBuV/m. This is believed to be due to the fact that point 306 is located in front of a building that is further back than adjacent buildings, thus reducing the shielding effect and increasing the reception level.

In the fourth mode, the reception level is measured at points 359, 144 and 145 shown in Fig. 17A. At each point, signals may be received from at least three base stations A-270, A-330, A-30. Fig. 17B shows reception levels received from respective base stations at various points. Here, since the points 359, 144 and 145 are close to each other, generally, they show similar tendencies in their reception levels, but actually, the reception level from the base station A-30 at the point 359 is 78.3 dBuV/m, high The received level tends to the expected inferred value of 70dBuV/m from other points. This is considered to be because the radio wave at point 359 is reflected by a tall building located in a group of low buildings, and thus the reception level is higher than that of point 144 directly under the building or point 145 separated from the building.

In the fifth mode, reception levels are measured at points 356, 221 and 224 shown in Fig. 18A. At each point, signals may be received from at least three base stations A-30, A-90, A-150. Fig. 18B shows reception levels received from respective base stations at various points. Here, since the points 356, 221 and 224 are close to each other, generally, they show similar tendencies in their reception levels, but actually, the reception level from the base station A-150 at the point 356 is 40.4 dBuV/m, high The received level tends to the expected inference value of 35dBuV/m from other points. This is considered to be due to the fact that point 356 is located in front of a low building in a tall building complex, thus reducing the shielding effect and thus improving the reception level.

In the sixth mode, the reception level is measured at points 346, 29 and 30 shown in Fig. 19A. At each point, signals may be received from at least three base stations A-30, A-90, A-150. Fig. 19B shows reception levels received from respective base stations at respective points. Here, since the points 346, 29 and 30 are close to each other, generally, they show similar trends in their reception levels, but actually, the reception levels from the base stations A-30, A-150 at the point 346 are 75.2 dBuV /m and 74.7dBuV/m, which is higher than the inferred value 70dBuV/m from other points where the received level tends to be expected. This is thought to be due to the fact that point 346 was affected by a large scale irregularly shaped building, thus increasing the reception level.

In the seventh mode, reception levels are measured at points 310, 200 and 201 shown in Fig. 20A. At each point, signals may be received from at least three base stations A-30, A-90, A-150. Fig. 20B shows reception levels received from respective base stations at various points. Here, since the points 310, 200 and 201 are close to each other, generally, they show similar tendencies in their reception levels, but actually, the reception level from the base station A-90 at the point 310 is 51.8 dBuV/m, high The received level tends to the expected inferred value of 47dBuV/m from other points. This is considered to be due to the fact that point 310 is affected by large scale irregularly shaped buildings, thus increasing the reception level.

In the eighth mode, the reception level is measured at points 343, 358 and 6 shown in Fig. 21A. At each point, signals may be received from at least three base stations A-120, A-180, A-240. Fig. 21B shows reception levels received from respective base stations at respective points. Here, since points 343, 358, and 6 are close to each other, generally, they show similar tendencies in their reception levels, but actually, the reception level from base station A-180 at point 343 is 58.3 dBuV/m, high The received level tends to the expected inferred value of 53dBuV/m from other points. This is believed to be due to the point 358 being located in the vicinity of a building of a different material (building with reflective surface walls), thus increasing the reception level due to reflections from the reflective wall.

(amendment 2)

In the first example, the processing is composed in such a way that a common standard deviation σ (variance σ ² ) is used at step S104 for all base stations CSi.

However, short-sectional changes in reception levels do not always follow the same probability distribution of fluctuations, and there is a high possibility that fluctuations will also change if the surrounding environment of the base station or the like changes.

Therefore, in this second modification, the processing is constituted in such a way that the probability density function P _xs (Γ _i ) is determined using the standard deviation σ _i set independently for each base station so that it is different from the short-section change fluctuation of each base station. The case is compatible.

The standard deviation of each base station can be set according to the system design, and it is also possible to independently estimate and pre-set the standard deviation, for example, based on the simulation value obtained from the radio wave of the surrounding environment of the base station with reference to the reflection of three-dimensional map information, etc., or based on the short section varying measured values, or both from simulated and measured values, in a similar manner to the propagation characteristic parameter α _i , etc. The standard deviation thus set is associated with the base station and stored in the base station database 91 (see FIG. 3(b)).

In this second embodiment, step S104 implemented by the position recognition means 92 is composed as follows. Refer to the base station database 91 to read the standard deviation σ _i corresponding to the base station CSi. Therefore, the probability density function P _xs (Γ _i ) is determined according to the following equation (5). Other steps are the same as in the first example.

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; xs</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; \{</span><span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; im</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; Xs</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; CSi</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; \}</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Similar to the propagation characteristic parameter α _i in the first modification, a composition may be adopted in which a plurality of σ _i values are stored for each base station based on at least one of time information, traffic density, assumed position, etc. (see FIG. 6 ). If storing a plurality of σ _i values for assumed positions, desirably, reception levels are simultaneously measured using a plurality of receiving antennas, and a database is created by dividing the measurement positions by the order of meters from the measured values of short-section changes. In this example, by determining the probability density function P _xs (Γ _i ) using the α _i equivalent and σ _i corresponding to the assumed position, it is possible to determine the probability density that takes into account the presence of buildings, etc., or terrain conditions between the assumed position and the base station Function P _xs (Г _i ).

In this way, by adopting a composition method using standard deviation values set independently for each base station, position recognition accuracy can be improved.

(amendment 3)

In the first example, in step S106, the likelihood L _xo (Γ _1o , Γ _2o , ... Γ _No ) is determined according to the joint probability density function represented by the multiplication of the probability density functions of the respective base stations.

In some examples, frequency efficiency and number of channels are increased by using directional antennas to divide an area or cell into multiple sectors (eg, 3 120° sectors or 6 60° sectors), depending on the base station. By adopting this division of sectors, limited channels can be allocated dynamically to each sector, thereby allowing efficient channel allocation and use to sectors with concentrated services in the case of heavy traffic conditions. If the base station has sectors organized in this way, the reception level from the base station will depend on the sector orientation angle.

Therefore, in this third modification, if the base station has a sector composition, the processing is composed in such a way while considering the influence of the direction angle of the sector to determine the likelihood. More specifically, step S106 implemented by the position recognition device 92 is constituted as follows. Determining the joint probability, which is a function of the angular deviation between the line connecting the assumed position Xs and the base station, and the sector direction angle, and _Lxs (Г _1o , Г _2o , .. ...Г _No ) values are multiplied, and the result of the multiplication is used as a new L _xs (Г _1o , Г _2o , ...Г _No ) value. Other steps are the same as in the first example.

In this way, by adopting a composition method that considers the influence of the sector direction angle when determining the likelihood, the position identification accuracy can be further improved.

(amendment 4)

In the first example, in step S107, the assumed position with the maximum likelihood is selected and identified as the position of the mobile terminal.

Sometimes, however, it is not always the case that only one putative position has a very prominent likelihood, but similar likelihood values are determined for a plurality of putative positions. In this case, it can be seen that selecting a number of hypothetical positions with a likelihood greater than a predetermined threshold enables a more certain identification of the position than selecting only one hypothetical position with the greatest likelihood.

Therefore, in the fourth modification, step S107 performed by the position recognition device 92 is constituted as follows. Select a plurality of hypothetical positions with likelihood values L _xs (Γ _1o , Γ _2o , ... Γ _No ) exceeding a predetermined threshold, determine an estimated area according to the selected plurality of assumed positions, and identify the estimated area as The location of the mobile terminal 10. Other steps are the same as in the first example.

Conceivable methods of determining the estimated area include, for example, setting the estimated area as a circular area with a minimum radius that includes all of the selected plurality of hypothetical positions, or setting the estimated area as having a predetermined radius in a plurality of selected hypothetical positions. The average position of the assumed position is set as the circular area of the center. When the estimated area is determined, desirably, those selected hypothetical locations that are far from the average location or the like should be excluded.

(experimental results)

The results of applying the position recognition method according to the present invention will be described below. Here, it is assumed that three waveforms are simultaneously observed for each measurement due to multipath effects within the mobile terminal, and the average value of the three observed values is used as the measured value.

(Simulation result 1)

The likelihood distribution was found by performing simulation of the position recognition method of the present invention under the following conditions.

(1) Treat the propagation characteristic parameter as a common characteristic for all base stations, A=130, α=3.4.

(2) Assume that the short-section variation follows a decibel-normal distribution, whose standard deviation is common to all base stations and has a value of α=6 (dB).

(3) Base stations are distributed according to the cellular model, the cell radius is 500m, and the number of base stations is 19 (see FIG. 7 ).

(4) The mobile terminal is located in the middle of the central cell.

(5) The approximate location range set in step S100 is taken as the central cell.

Fig. 8 shows the determined likelihood distributions in the case of selecting 1-3 base stations as base stations CSj according to the present invention. In this figure, BS represents a base station, MS represents a mobile terminal, and the height of the three-dimensional graph represents the likelihood. It can be seen from the figure that when the number of base stations increases, the tendency of the likelihood distribution increases. This means that the greater the number of base stations, the greater the possibility of more precisely identifying the location of the mobile terminal. If the assumed position with the maximum likelihood is taken as the estimated position of the mobile terminal in the case of three base stations, an estimation error of 11 m will result.

(Simulation result 2)

Likelihood errors were found by performing simulation of the position recognition method of the present invention under the following conditions.

(1) Treat the propagation characteristic parameter as a common characteristic for all base stations, A=130, α=3.4.

(2) Assuming that the short-section changes follow a decibel-normal distribution, its standard deviation is α=6(dB) for all base stations in case 1, and has a value of α=6(dB) only for the central cell in case 2, and the other cells is α=4(dB).

(3) Base stations are distributed according to the cellular model, the cell radius is 500m, and the number of base stations is 19 (see FIG. 7 ).

(4) According to the uniform distribution, the mobile terminal is located in the central cell, and 500 sets of measurement data are generated.

(5) The approximate location range set in step S100 is taken as the central cell.

FIG. 9 shows estimation errors in Case 1 and Case 2 when 3-10 base stations are selected as base station CSj. Here, the estimation error is an estimation error at a point where the cumulative probability is 0.67. It can be seen from the figure that the greater the number of base stations, the higher the accuracy of identifying the position, and by setting the value of σ to a lower value in a cell other than the central cell, the accuracy of identifying the position can become higher.

(Simulation result 3)

Simulations of the position recognition method and the SX method according to the present invention were performed under the following conditions, and estimation errors were compared.

(1) Treat the propagation characteristic parameter as a common characteristic for all base stations, A=130, α=3.4.

(2) It is assumed that the variation of the short section follows a decibel-normal distribution, and its standard deviation is α=6 (dB) common to all base stations.

(3) Base stations are distributed according to the cellular model, the cell radius is 500m, and the number of base stations is 19 (see FIG. 7 ).

(4) According to the uniform distribution, the mobile terminal is located in the central cell, and 500 sets of measurement data are generated.

(5) The approximate location range set in step S100 is taken as the central cell.

Fig. 10 shows the estimation errors of each method in the case of selecting 3 to 10 base stations as the base station CSj. Here, the estimation error is an estimation error at a point where the cumulative probability is 0.67. In addition, FIG. 11 shows the relationship between the estimation error and the cumulative probability in each method in the case where 3 or 4 base stations are selected as the base station CSj. It can be seen from these figures that the position recognition method according to the present invention enables a position to be recognized with higher accuracy than the SX method.

(results based on measured values)

Here, results of applying the position recognition method of the present invention and a general method not assuming a probability distribution are shown based on data measured in the following conditions.

(1) Measurement date: May 11, 2001

(2) Measurement locations: 45 locations around Hon-machi, Chuo-ku, Osaka.

(3) Measurement details: The reception levels of radio waves from surrounding base stations were measured for a total of 448 measurements (approximately 10 times per location).

FIG. 12 shows the relationship between the estimation error and the cumulative probability in each method in the case of selecting three base stations as the base station CSj. Furthermore, FIG. 13 shows details of the results of the position recognition method according to the present invention. From this figure, it can be seen that the position recognition method according to the present invention enables the position to be recognized with a higher degree of accuracy in terms of measurement data than the conventional method.

(second embodiment)

Next, a second embodiment of the present invention will be described. A second embodiment includes a storage medium for storing a location recognition program. A CD-ROM, magnetic disk, semiconductor memory, or other medium can be used as this storage medium. The location identification program is read from the storage medium into the data processing device, and the operation of the data processing device is controlled. Under the control of the position detection program, the data processing means executes at least the same processing as that of the position identification means 92 of the central station in the first embodiment.

(other embodiments)

The present invention is not limited to the above-described embodiments, but can also be applied to various modifications. For example, in the above-mentioned embodiment, the configuration is adopted in which the reception level of radio waves from the base station is measured at the mobile terminal 10, but the configuration in which the reception level of radio waves from the mobile terminal is measured at the base station can also be employed. In this example, the parameters A and α, the standard deviation σ of short-section variation, etc. are determined according to the functional composition of the mobile terminal.

Furthermore, in the above-described embodiment, the composition method of calculating the likelihood from the reception level is adopted, but a composition method of calculating the likelihood from the delay time instead of the reception level may also be used.

In addition, in the above embodiments, for example, the location of the mobile terminal is detected at the central station, and a composition method of performing detection processing at the mobile terminal (or base station) can also be adopted. In this example, the mobile terminal (or base station) may include a base station database, or at least be contained in such a way as to enable access to the base station database.

According to the present invention, since at each assumed position, the probability distribution for the short-section change of the radio wave reception level from the base station is assumed, the likelihood of the reception level measurement value corresponding to the mobile terminal measurement is determined based on the probability distribution, and The assumed position with the maximum likelihood value is designated as the position of the mobile terminal, and the position of the mobile terminal can be determined as the maximum likelihood estimation value according to the assumption that the change of the short section follows the probability distribution.

In addition, since the location recognition accuracy can be improved by setting a small mesh size, on the contrary, the number of assumed positions to be processed can be reduced, thereby reducing the calculation burden by setting a large mesh size, and by adjusting the mesh size, it is possible to obtain Flexible control of the trade-off between computational burden and positional accuracy.

The entire disclosure of Japanese Patent Application No. 2001-270217 filed on September 6, 2001 including specification, claims, drawings and abstract is hereby incorporated by reference in its entirety.

Claims (14)

1. A position detection method for a mobile communication system, the mobile communication system is composed of a plurality of base stations, a mobile terminal and a control station, in this system, a base station transmits a signal containing each A unique number of base stations, the mobile terminal receives the signal from one or more of the N base stations, measures and stores the reception level Γi from the i-th base station, and transmits the first to Nth reception levels via the communication base station To the control station, at the control station, based on the database created based on the base station number, the coordinates of the base station, and the reception level previously obtained by measurement or radio wave propagation simulation or measurement and simulation at each point within the service area of each base station, and the data sent by the mobile terminal Receiving level to identify the position of the mobile terminal, the position detection method is characterized in that it comprises the steps of: Set two-dimensional or three-dimensional mesh shape or grid shape coordinate points in the entire service area of each base station; At each candidate point from the coordinate points, an estimated value of the average reception level is determined corresponding to each base station receiving the signal (hereinafter referred to as "signal receiving base station"), and the estimated value is taken as the average value, determining the probability density function of said mean variation; Integrating the probability density function determined for each of the candidate points at each of the candidate points for each signal receiving base station according to the measured reception level within a given range; Determine the likelihood of the candidate point by multiplying the values obtained by integrating the signal receiving base stations at each of the candidate points; and The candidate point with the maximum likelihood value is detected as the estimated position of the mobile terminal. 2. The position detection method according to claim 1, comprising the steps of: At each candidate point from said coordinate point, determine an average reception level estimated value corresponding to each base station receiving said signal (hereinafter referred to as "signal receiving base station"), using said estimated value as an average value, and determining a probability density function of said mean variation; According to the reception level measured for each signal receiving base station at each candidate point, substitute the measured reception level into the probability density function determined for each candidate point; determining the likelihood of said candidate points by multiplying, at each of said candidate points, values obtained by said replacement of said respective signal receiving base stations; and The candidate point with the maximum likelihood value is detected as the estimated position of the mobile terminal. 3. The position detection method according to claim 2, comprising the steps of: At each candidate point from said coordinate point, determine an average reception level estimated value corresponding to each base station receiving said signal (hereinafter referred to as "signal receiving base station"), using said estimated value as an average value, and determining a probability density function of said mean variation; Substituting the measured data into said probability density function determined for each candidate point based on said reception level measured for each signal receiving base station at each candidate point, and thereby multiplying the value obtained by specifying the value; Determining the likelihood of the candidate points by multiplying the values obtained by the replacement of the signal receiving base stations at each of the candidate points; and The candidate point with the maximum likelihood value is detected as the estimated position of the mobile terminal. 4. The position detection method according to claim 1, wherein the likelihood is calculated by independently estimating parameters related to the probability density function and the propagation characteristic equation for each corresponding base station. 5. The position detection method according to claim 1, wherein the likelihood is calculated by independently estimating parameters related to the probability density function and the propagation characteristic equation for each cell or each grid point. 6. The position detection method according to claim 1, characterized in that, according to the time of day, the date of the week, the season of the year, traffic density, etc., the likelihood is calculated using propagation characteristic equations with different probability density functions, where In this case, the desired propagation characteristics take into account building and terrain conditions, etc. 7. The position detection method according to claim 1, characterized in that, in the case that the base station has a sector component, the coordinates and the sector direction angle are linked together, and the joint probability is set, and its factor is each sector direction The difference between the angles, and the likelihood is determined by multiplying the joint probability by the likelihood value corresponding to the coordinate. 8. The position detection method according to claim 1, characterized in that, according to building information or map information, the long-section propagation estimation equation and the short-section propagation estimation equation are determined from the propagation characteristics considering the building or terrain conditions between the coordinate point and the base station Cross-sectional mean probability density. 9. The position detection method according to claim 1, wherein a plurality of estimated positions whose likelihoods are higher than a specified value are detected as estimated areas. 10. The position detection method according to claim 1, characterized in that a propagation delay time is used instead of a reception level. 11. A computer-readable storage medium storing a position detection program for causing a computer to execute the position detection method according to claim 1. 12. A position detection program for causing a computer to execute the position detection method according to claim 1. 13. A method for creating a database for detecting the position of a mobile terminal in a mobile communication system consisting of a plurality of base stations, mobile terminals and a control station, characterized in that the The method includes the steps of: storing as data the number of the base station, the coordinates of the base station and the reception levels previously obtained by measurement or simulation of radio wave propagation or measurement and simulation at various points within the service area of each base station in an interconnected manner; based on said stored data, detecting a characteristic point at which the reception level measured is unique when compared with reception levels at other nearby points; and and storing propagation characteristic parameters corresponding to the detection characteristic points. 14. A method of creating a database for detecting the position of a mobile terminal in a mobile communication system comprising a plurality of base stations, mobile terminals and a control station, characterized in that in order to determine a parameter of a probability density function Standard deviation, using and measuring multiple receiving antennas simultaneously, creates a database by identifying the measurement locations in meters.

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