CN103379619A - Method and system for positioning - Google Patents

Abstract

The invention provides a method for positioning, which includes: carrying out positioning based on an access point of a wireless local network, and obtaining an estimated initial position of a user device; obtaining course angle and speed information of the user device; and correcting the estimated initial position according to the course angle and speed information, and obtaining final position information. The invention also provides a system for positioning. With the method and the system for positioning, positioning error caused by strength floating of received information and accumulative error caused by sensor noise are corrected, so that a combined positioning system with low cost and high precision is obtained.

Description

A kind of localization method and system

Technical field

The present invention relates to the wireless network positioning field, relate in particular to a kind of localization method and system.

Background technology

It is low to have a cost based on the navigation system of WLAN, and precision is high, and the advantages such as applied range (indoor and outdoors) have obtained very large success such as aspects such as emergency relief, intelligent transportation and indoor positioning navigation in location-based service.But still exist following two problems to need solution badly: WLAN positioning accuracy that the received signal strength (Received Signal Strength, RSS) that the factors such as (1) multipath interference cause has floated severe exacerbation; (2) in the zone that wireless access node (Access Point, AP) does not cover, because the AP disappearance causes the WLAN locate failure.

In order to address the above problem, people have proposed several different methods, can be divided into following three classes:

1, the WLAN navigation system of time-based diversity and probability Distribution Model

The basic thought of the WLAN navigation system of time-based diversity and probability Distribution Model is: the fixed position utilizes time diversity to obtain a plurality of samples of received signal strength in locating area, set up the probability Distribution Model of received signal strength according to a plurality of sample informations, the probability Distribution Model of received signal strength is stored in the property data base; At positioning stage, moving target utilizes time diversity to obtain a plurality of samples of received signal strength, obtains stable received signal strength and positions by asking for sample average.Because time diversity need to consume a large amount of time, increased the location and postponed, can't realize real-time location, in running fix, can't use.

2. based on the WLAN navigation system of Kalman filtering

Basic thought based on the WLAN navigation system of Kalman filtering is: at first utilize the WLAN location algorithm to obtain the location estimation of moving target, then utilize the speed of moving target track continuity or supposition moving target within the specific limits, state equation and the observational equation of structure Kalman filter carry out filtering to user's location estimation and process.Although this method has improved the positioning accuracy of WLAN navigation system, owing to set in advance moving target speed, therefore can't realize adaptive-filtering, limited application in practice.Can not solve simultaneously because the WLAN locate failure that the AP disappearance causes.

3.WLAN/GPS integrated positioning

Because GPS in outdoor open space, can obtain good positioning accuracy, therefore in the zone of AP disappearance, can utilize GPS to obtain accurately positional information; Simultaneously in built-up urban district, can utilize WLAN to locate to remedy the deficiency of GPS location, can say that the WLAN/GPS integrated positioning obtained good positioning performance under outdoor environment, but under indoor environment, because gps signal is blocked, and can't improve the WLAN indoor position accuracy.

Summary of the invention

The technical problem to be solved in the present invention provides a kind of localization method and system, solves the existing inadequate problem of Positioning System.

In order to address the above problem, the invention provides a kind of localization method, comprising:

Access point based on WLAN (wireless local area network) positions, and obtains the initial estimated location of subscriber equipment;

Obtain course angle and the velocity information of described subscriber equipment;

According to described course angle and velocity information described initial estimated location is revised, obtained final position information.

Further, said method also can have following characteristics, and described access point based on WLAN (wireless local area network) positions and comprises:

Choose reference point, measure signal strength signal intensity from each access point in each reference point, the sign/position of the position of described reference point, described signal strength signal intensity and corresponding access point is stored in the database; The signal strength signal intensity of each access point around subscriber equipment to be positioned is measured is searched database and is obtained corresponding reference point set, mates the initial estimated location of determining subscriber equipment with described reference point set.

Further, said method also can have following characteristics, and described and reference point set is mated the initial estimated location of determining described subscriber equipment and comprised:

Select the reference point of the Euclidean distance minimum of m received signal strength, use a described m reference point the position linear weighted function and as the initial estimated location of described subscriber equipment, described m is more than or equal to 1.

Further, said method also can have following characteristics, and described course angle and the velocity information of obtaining described subscriber equipment comprises:

Obtain described course angle and velocity information according to the metrical information of MARG transducer.

Further, said method also can have following characteristics, and described metrical information according to the MARG transducer is obtained described course angle and comprised:

Metrical information according to the magnetometer of described MARG transducer is obtained the first course angle φ _Mag, obtain the second course angle according to the gyrostatic metrical information of described MARG transducer

Obtain the course angle φ of described subscriber equipment according to described the first course angle and the second course angle:

$\mathrm{\φ}=(1-W){\mathrm{\φ}}_k^{\mathrm{gyr}}+{\mathrm{W\φ}}_{\mathrm{mag}}$

Wherein, described W is the weighted value of presetting, 0≤W≤1.

Further, said method also can have following characteristics, and described method also comprises, obtains roll angle, the angle of pitch of described subscriber equipment according to the metrical information of described MARG transducer;

Describedly according to described course angle and velocity information described initial estimated location correction is comprised:

With the input of described roll angle, the angle of pitch, course angle and the velocity information of obtaining according to the metrical information of described MARG transducer as Kalman filter, carry out Kalman filtering, export new course angle and velocity information;

With the input as particle filter of the course angle of described Kalman filter output and velocity information and described initial estimated location, carry out particle filter, output position information, course angle and velocity information, with the positional information of output as the final position information of described subscriber equipment.

Further, said method also can have following characteristics, and the described Kalman filtering of carrying out comprises:

Carry out the state one-step prediction of Kalman filtering,

Calculate the predicated error variance matrix

The calculation of filtered gain matrix

Carry out state estimation ${\mathrm{\φ}}_k={\mathrm{\φ}}_k^{-}+K_k[{\mathrm{\φ}}_{\mathrm{PF}}-{\mathrm{\φ}}_k^{-}];$

Calculate estimation error variance

Wherein, described

The angular speed that represents the gyroscope output of described MARG transducer, φ _K-1The course angle of obtaining according to described MARG transducer during expression moment k-1, Δ T represents the measuring intervals of TIME of described MARG transducer, Q and R represent respectively process noise and measure the covariance matrix of noise, K _kBe the Kalman filter gain, And P _kThe expression varivance matrix, φ _PFThe course angle of particle filter output when locating for the last time, first φ during Kalman filtering _PFBe designated value.

Further, said method also can have following characteristics, and the described particle filter that carries out comprises:

When carrying out particle filter first, need the initialization particle, adopt Gaussian Profile to come the probability density function of initialization particle;

According to described course angle and velocity information, and described initial estimated location, predict next step state information of described subscriber equipment:

$\left[\begin{array}{c}{x}_{k+1}\\ y_{k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& T_s\·\cos \left({\mathrm{\φ}}_k\right)\\ 0& 1& T_s\·\sin \left({\mathrm{\φ}}_k\right)\end{array}\right]\left[\begin{array}{c}{x}_k\\ y_k\\ v_k\end{array}\right]+\left[\begin{array}{cc}\frac{T_s^2}{2}& 0\\ 0& \frac{T_s^2}{2}\end{array}\right]\left[\begin{array}{c}{\mathrm{\η}}_x\\ {\mathrm{\η}}_y\end{array}\right]$

Calculate weight and the normalization of each particle, as follows:

${\mathrm{\ω}}_{k+1}^i=\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp [-\frac{\left|\right|x_k^z-x_k^i\left|\right|}{{2\mathrm{\σ}}^2}]$

${\mathrm{\ω}}_{k+1}^i=\frac{{\mathrm{\ω}}_{k+1}^i}{\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_{k+1}^j}$

Carry out particle and resample, as the particle of particle filter next time;

Wherein, described [x _k, y _k] ^TBe the state vector of each particle, T _sThe expression last time is based on location and this time interval based on the location of the access point of WLAN (wireless local area network) of the access point of WLAN (wireless local area network), φ _kRepresent described course angle, v _kRepresent described velocity information, [η _x, η _y] ^TThe expression acceleration, with the simulation of the Gaussian noise of zero-mean, variance is by the metrical information estimation of described MARG transducer,

For inputting the state value of described particle filter,

Represent i particle at the state value of moment k, σ represents the noise variance of signal strength measurement.

The present invention also provides a kind of navigation system, comprising:

The WLAN locating module is used for positioning based on the access point of WLAN (wireless local area network), obtains the initial estimated location of subscriber equipment;

The sensor localization module is for course angle and the velocity information of obtaining described subscriber equipment;

Fusion Module is used for according to described course angle and velocity information described initial estimated location being revised, and obtains final position information.

Further, said system also can have following characteristics, and described WLAN locating module comprises:

Database is used for being stored in the signal strength signal intensity from each access point that each reference point is measured, the position of described reference point and the sign/position of corresponding access point;

The RSS measuring unit is used for measuring subscriber equipment to be positioned and measures the signal strength signal intensity of each access point on every side;

Positioning unit is used for the signal strength signal intensity according to described RSS measuring unit measurement, searches database and obtains corresponding reference point set, mates the initial estimated location of determining subscriber equipment with described reference point set.

Further, said system also can have following characteristics, and described positioning unit and reference point set are mated the initial estimated location of determining described subscriber equipment and comprised:

Described positioning unit is selected the reference point of the Euclidean distance minimum of m received signal strength, use a described m reference point the position linear weighted function and as the initial estimated location of described subscriber equipment, described m is more than or equal to 1.

Further, said system also can have following characteristics, and described sensor localization module comprises: MARG transducer and data processing unit, wherein:

Described MARG transducer is used for, and described subscriber equipment is measured, and obtains metrical information;

Described data processing unit is used for, and obtains described course angle and velocity information according to the metrical information of MARG transducer.

Further, said system also can have following characteristics, and described data processing unit obtains described course angle according to the metrical information of MARG transducer and comprises:

Described data processing unit obtains the first course angle φ according to the metrical information of the magnetometer of described MARG transducer _Mag, obtain the second course angle according to the gyrostatic metrical information of described MARG transducer

Obtain the course angle φ of described subscriber equipment according to described the first course angle and the second course angle:

$\mathrm{\φ}=(1-W){\mathrm{\φ}}_k^{\mathrm{gyr}}+{\mathrm{W\φ}}_{\mathrm{mag}}$

Wherein, described W is the weighted value of presetting, 0≤W≤1.

Further, said system also can have following characteristics, and described Fusion Module comprises: Kalman filter and particle filter, wherein:

Described data processing unit also is used for, and obtains roll angle, the angle of pitch of described subscriber equipment according to the metrical information of described MARG transducer;

Described Kalman filter is used for, described roll angle, the angle of pitch, course angle and the velocity information of obtaining according to the metrical information of described MARG transducer inputted as the state value of Kalman filter, carry out Kalman filtering, export new course angle and velocity information;

Described particle filter is used for, course angle and velocity information with described Kalman filter output, and described initial estimated location is inputted as state value, carry out particle filter, output position information, course angle and velocity information, with the positional information of output as the final position information of described subscriber equipment.

Further, said system also can have following characteristics, and described Kalman filter is carried out Kalman filtering and comprised:

Carry out the state one-step prediction of Kalman filtering,

Calculate the predicated error variance matrix

The calculation of filtered gain matrix $K_k=P_k^{-}\·{[P_k^{-}+R]}^{-1};$

Carry out state estimation ${\mathrm{\φ}}_k={\mathrm{\φ}}_k^{-}+K_k[{\mathrm{\φ}}_{\mathrm{PF}}-{\mathrm{\φ}}_k^{-}];$

Calculate estimation error variance

Wherein, described

The angular speed that represents the gyroscope output of described MARG transducer, φ _K-1The course angle of obtaining according to described MARG transducer during expression moment k-1, Δ T represents the measuring intervals of TIME of described MARG transducer, Q and R represent respectively process noise and measure the covariance matrix of noise, K _kBe the Kalman filter gain,

And P _kThe expression varivance matrix, φ _PFThe course angle of particle filter output when locating for the last time, first φ during Kalman filtering _PFBe designated value.

Further, said system also can have following characteristics, and the described particle filter that carries out comprises:

The initialization particle adopts Gaussian Profile to come the probability density function of initialization particle;

According to described course angle and velocity information, and described initial estimated location, predict next step state information of described subscriber equipment:

$\left[\begin{array}{c}{x}_{k+1}\\ y_{k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& T_s\·\cos \left({\mathrm{\φ}}_k\right)\\ 0& 1& T_s\·\sin \left({\mathrm{\φ}}_k\right)\end{array}\right]\left[\begin{array}{c}{x}_k\\ y_k\\ v_k\end{array}\right]+\left[\begin{array}{cc}\frac{T_s^2}{2}& 0\\ 0& \frac{T_s^2}{2}\end{array}\right]\left[\begin{array}{c}{\mathrm{\η}}_x\\ {\mathrm{\η}}_y\end{array}\right]$

Calculate weight and the normalization of each particle, as follows:

${\mathrm{\ω}}_{k+1}^i=\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp [-\frac{\left|\right|x_k^z-x_k^i\left|\right|}{{2\mathrm{\σ}}^2}]$

${\mathrm{\ω}}_{k+1}^i=\frac{{\mathrm{\ω}}_{k+1}^i}{\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_{k+1}^j}$

Carry out particle and resample, as the particle of particle filter next time;

Wherein, described [x _k, y _k] ^TBe the state vector of each particle, T _sThe expression last time is based on location and this time interval based on the location of the access point of WLAN (wireless local area network) of the access point of WLAN (wireless local area network), φ _kRepresent described course angle, v _kRepresent described velocity information, [η _x, η _y] ^TThe expression acceleration, with the simulation of the Gaussian noise of zero-mean, variance is by the metrical information estimation of described MARG transducer,

Be the state value of the described particle filter of current input,

Represent i particle at the state value of moment k, σ represents the noise variance of signal strength measurement.

The present invention utilizes MARG (Magnetic, Angular Rate, and Gravity, magnetometer, gyroscope and accelerometer) auxiliary WLAN (Wireless-LAN) navigation system of transducer, designed a data anastomosing algorithm based on particle filter and Kalman filtering, this blending algorithm takes full advantage of the complementary characteristic of WLAN (Wireless-LAN) and MARG location technology, effectively proofreaied and correct by receiving the unsteady position error that causes of information strength and by the accumulated error that sensor noise causes, having realized the WLAN/MARG integrated positioning system of a low-cost and high-precision.

Description of drawings

Fig. 1 is embodiment of the invention navigation system block diagram;

Fig. 2 is embodiment of the invention localization method flow chart.

Embodiment

For making the purpose, technical solutions and advantages of the present invention clearer, hereinafter in connection with accompanying drawing embodiments of the invention are elaborated.Need to prove that in the situation of not conflicting, the embodiment among the application and the feature among the embodiment be combination in any mutually.

The embodiment of the invention provides a kind of localization method, comprising:

Access point based on WLAN (wireless local area network) positions, and obtains the initial estimated location of subscriber equipment;

Obtain course angle and the velocity information of described subscriber equipment;

According to described course angle and velocity information described initial estimated location is revised, obtained final position information.

Wherein, described access point based on WLAN (wireless local area network) positions and comprises:

Choose reference point, measure signal strength signal intensity from each access point in each reference point, the sign/position of the position of described reference point, described signal strength signal intensity and corresponding access point is stored in the database; The signal strength signal intensity of each access point around subscriber equipment to be positioned is measured is searched database and is obtained corresponding reference point set, mates the initial estimated location of determining subscriber equipment with described reference point set.

Wherein, the set of described and reference point is mated the initial estimated location of determining described subscriber equipment and is comprised:

Select the reference point of the Euclidean distance minimum of m received signal strength, use a described m reference point the position linear weighted function and as the initial estimated location of described subscriber equipment, described m is more than or equal to 1.

Wherein, described course angle and the velocity information of obtaining described subscriber equipment comprises:

Obtain described course angle and velocity information according to the metrical information of MARG transducer.

Wherein, described metrical information according to the MARG transducer is obtained described course angle and is comprised:

Metrical information according to the magnetometer of described MARG transducer is obtained the first course angle φ _Mag, obtain the second course angle according to the gyrostatic metrical information of described MARG transducer

Obtain the course angle φ of described subscriber equipment according to described the first course angle and the second course angle:

$\mathrm{\φ}=(1-W){\mathrm{\φ}}_k^{\mathrm{gyr}}+{\mathrm{W\φ}}_{\mathrm{mag}}$

Wherein, described W is the weighted value of presetting, 0≤W≤1.

Wherein said method also comprises, obtains roll angle, the angle of pitch of described subscriber equipment according to the metrical information of described MARG transducer;

Describedly according to described course angle and velocity information described initial estimated location correction is comprised:

With the input of described roll angle, the angle of pitch, course angle and the velocity information of obtaining according to the metrical information of described MARG transducer as Kalman filter, carry out Kalman filtering, export new course angle and velocity information;

With the input as particle filter of the course angle of described Kalman filter output and velocity information and described initial estimated location, carry out particle filter, output position information, course angle and velocity information, with the positional information of output as the final position information of described subscriber equipment.

Certainly, also can not carry out Kalman filtering, directly will carry out particle filter according to course angle and velocity information that the MARG transducer obtains, obtain final positional information.

Except Kalman filtering and particle filter, also can use Bayes's filtering, complementary filter, EKF, the blending algorithms such as Federated Kalman Filtering.

Wherein, the described Kalman filtering of carrying out comprises:

Carry out the state one-step prediction of Kalman filtering,

Calculate the predicated error variance matrix

The calculation of filtered gain matrix $K_k=P_k^{-}\·{[P_k^{-}+R]}^{-1};$

Carry out state estimation ${\mathrm{\φ}}_k={\mathrm{\φ}}_k^{-}+K_k[{\mathrm{\φ}}_{\mathrm{PF}}-{\mathrm{\φ}}_k^{-}];$

Calculate estimation error variance

Wherein, described

The angular speed that represents the gyroscope output of described MARG transducer, φ _K-1The course angle of obtaining according to the MARG transducer during expression moment k-1, Δ T represents the measuring intervals of TIME of described MARG transducer, Q and R represent respectively process noise and measure the covariance matrix of noise, K _kBe the Kalman filter gain, And P _kThe expression varivance matrix, φ _PFThe course angle of particle filter output when locating for the last time, first φ during Kalman filtering _PFBe designated value.

Wherein, the described particle filter that carries out comprises:

When carrying out particle filter first, need the initialization particle, adopt Gaussian Profile to come the probability density function of initialization particle;

According to described course angle and velocity information, and described initial estimated location, predict next step state information of described subscriber equipment:

$\left[\begin{array}{c}{x}_{k+1}\\ y_{k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& T_s\·\cos \left({\mathrm{\φ}}_k\right)\\ 0& 1& T_s\·\sin \left({\mathrm{\φ}}_k\right)\end{array}\right]\left[\begin{array}{c}{x}_k\\ y_k\\ v_k\end{array}\right]+\left[\begin{array}{cc}\frac{T_s^2}{2}& 0\\ 0& \frac{T_s^2}{2}\end{array}\right]\left[\begin{array}{c}{\mathrm{\η}}_x\\ {\mathrm{\η}}_y\end{array}\right]$

Calculate weight and the normalization of each particle, as follows:

${\mathrm{\ω}}_{k+1}^i=\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp [-\frac{\left|\right|x_k^z-x_k^i\left|\right|}{{2\mathrm{\σ}}^2}]$

${\mathrm{\ω}}_{k+1}^i=\frac{{\mathrm{\ω}}_{k+1}^i}{\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_{k+1}^j}$

Carry out particle and resample, as the particle of particle filter next time;

Wherein, described [x _k, y _k] ^TBe the state vector of each particle, T _sThe expression last time is based on location and this time interval based on the location of the access point of WLAN (wireless local area network) of the access point of WLAN (wireless local area network), φ _kRepresent described course angle, v _kRepresent described velocity information, [η _x, η _y] ^TThe expression acceleration, with the simulation of the Gaussian noise of zero-mean, variance is by the metrical information estimation of described MARG transducer,

Be the state value of the described particle filter of current input,

Represent i particle at the state value of moment k, σ represents the noise variance of signal strength measurement.

The navigation system realization block diagram that the embodiment of the invention provides is seen shown in the accompanying drawing 1, comprising: the WLAN locating module, and sensor localization module and Fusion Module, wherein:

The WLAN locating module is used for positioning based on the access point of WLAN (wireless local area network), obtains the initial estimated location of subscriber equipment;

The sensor localization module is for course angle and the velocity information of obtaining described subscriber equipment;

Fusion Module is used for according to described course angle and velocity information described initial estimated location being revised, and obtains final position information.

Described WLAN locating module comprises:

Database is used for being stored in the signal strength signal intensity from each access point that each reference point is measured, the position of described reference point and the sign/position of corresponding access point;

The RSS measuring unit is used for measuring subscriber equipment to be positioned and measures the signal strength signal intensity of each access point on every side;

Positioning unit is used for the signal strength signal intensity according to described RSS measuring unit measurement, searches database and obtains corresponding reference point set, mates the initial estimated location of determining subscriber equipment with described reference point set.

Described positioning unit and reference point set are mated the initial estimated location of determining described subscriber equipment and are comprised:

Described positioning unit is selected the reference point of the Euclidean distance minimum of m received signal strength, use a described m reference point the position linear weighted function and as the initial estimated location of described subscriber equipment, described m is more than or equal to 1.

Described sensor localization module comprises: MARG transducer and data processing unit, wherein:

Described MARG transducer is used for, and described subscriber equipment is measured, and obtains metrical information;

Described data processing unit is used for, and obtains described course angle and velocity information according to the metrical information of described MARG transducer.

Described data processing unit obtains described course angle according to the metrical information of MARG transducer and comprises:

Described data processing unit obtains the first course angle φ according to the metrical information of the magnetometer of described MARG transducer _Mag, obtain the second course angle according to the gyrostatic metrical information of described MARG transducer

Obtain the course angle φ of described subscriber equipment according to described the first course angle and the second course angle:

$\mathrm{\φ}=(1-W){\mathrm{\φ}}_k^{\mathrm{gyr}}+{\mathrm{W\φ}}_{\mathrm{mag}}$

Wherein, described W is the weighted value of presetting, 0≤W≤1.

Described Fusion Module comprises: Kalman filter and particle filter, wherein:

Described data processing unit also is used for, and obtains roll angle, the angle of pitch of described subscriber equipment according to the metrical information of described MARG transducer;

Described Kalman filter is used for, described roll angle, the angle of pitch, course angle and the velocity information of obtaining according to the metrical information of described MARG transducer inputted as the state value of Kalman filter, carry out Kalman filtering, export new course angle and velocity information;

Described particle filter is used for, course angle and velocity information with described Kalman filter output, and described initial estimated location is inputted as state value, carry out particle filter, output position information, course angle and velocity information, with the positional information of output as the final position information of described subscriber equipment.

The concrete grammar of particle filter and Kalman filtering is referring to embodiment of the method.

Further specify the present invention below by concrete application example.

1) WLAN location

Fingerprinting (fingerprint characteristic) position fixing process based on WLAN mainly is divided into training and locates two stages as shown in Figure 2.

(1) training stage

Its target is to set up a location fingerprint identification database.Groundwork is fingerprint characteristic information---the RSS (Received Signal Strength, received signal strength) that gathers each reference point (Reference Point, RP) position in the area-of-interest.As shown in Figure 1, mobile subscriber (Mobile User, MU) successively in the RSS value of each reference point measurement from different AP, and corresponding MAC Address and latitude and longitude coordinates information stored in the database, until all reference points in the traversal area-of-interest, this process has been finished the measurement of RSS and the foundation of RP database.

Concrete:

Select reference point (RP), the indoor 2-3 rice of choosing of the spacing of reference, outdoor selection 8-11 rice at locating area;

Measure the signal intensity samples of WLAN access point by space diversity reception to communicate in reference point, the positional information (available latitude and longitude coordinates represents) of AP address or sign (such as, MAC Address) and corresponding reference point;

Be weighted filtering by signal intensity samples, obtain the finger print information of reference point RP, store in the fingerprint information data storehouse.

(2) positioning stage

(1) RSS (Received Signal Strength, received signal strength) of WLAN access point around the mobile subscriber measures, and data are carried out filtering process;

(2) in fingerprint database, search corresponding set of fingerprint information according to MAC Address;

(3) with the set in reference point mate calculating, determine mobile subscriber's position.The coupling calculating principle is the Euclidean distance of received signal strength, selects the reference point of m Euclidean distance minimum, with the linear weighted function of the coordinate of these reference points with represent mobile subscriber's position, is called initial estimated location.

Except above-mentioned coupling calculating principle, also has coupling based on nerual network technique, based on the matching principle of histogram probabilistic synchronization algorithm, based on the matching principle of SVMs etc.

One example is as follows:

The RSS of AP around the mobile subscriber measures mates calculating with itself and pre-stored RSS vector in database, and matching principle is the Euclidean distance of received signal strength, shown in (1):

$D_j=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{({\mathrm{rss}}_i-{\mathrm{RSS}}_{\mathrm{ji}})}^2}---\left(1\right)$

D wherein _jEuclidean distance or the similarity of signal strength signal intensity between expression reference point j and the mobile subscriber, D _jLess apart nearer of showing between the two; Rss=(rss _i, rss ₂..., rss _n) the current measurement of vector representation mobile subscriber to the RSS of n AP; RSS=(RSS _J1, RSS _J2..., RSS _Jn) vector representation stores RSS in the database into j reference point, represents the finger print information of reference point j.

Select the reference point (x of m Euclidean distance minimum ₁, x ₂..., x _m), with the linear weighted function of these reference point coordinates with represent the position coordinates x that the mobile subscriber is current ₀=(x ₀, y ₀), computing formula is as follows:

W wherein _kThe weight of expression reference point k, computing formula is as follows:

$w_k=\frac{1}{D_k}/\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\frac{1}{D_k^2}---\left(3\right)$

Above-mentioned WLAN localization method only is example, and other WLAN localization methods also can be applicable in the invention.

2) the MARG sensing data is processed

It is to utilize the information such as acceleration that transducer provides and angular speed that the MARG sensing data is processed, and obtains the information such as the attitude angle of carrier and relative position.At first the intelligent terminal with integrated three-dimensional gyroscope, three-dimensional accelerometer and three-dimensional magnetometer is defined into an x-y-z coordinate system, be commonly referred to as carrier coordinate system, the center of gravity of getting carrier is the carrier coordinate system initial point, and three axles coincide with the longitudinal axis, transverse axis and the vertical pivot of carrier respectively.Corresponding absolute coordinate system is commonly referred to X-Y-Z navigation coordinate system with it, and X, Y, Z axis distribute and point to east, north, sky, follow the right-hand rule.

For example: roll angle

Pitching angle theta and course angle φ represent respectively the corner that carrier coordinate system is rotated around x-axis, y-axis and z-axis, are used for representing that carrier coordinate system with respect to the orientation of navigation coordinate system, is also referred to as the attitude angle of carrier.The computing formula of roll angle and the angle of pitch is as follows:

$\mathrm{\θ}=\arcsin \frac{{\stackrel{..}{x}}^{\′}}{{\left({\stackrel{..}{x}}^{\′}\right)}^2+{\left({\stackrel{..}{y}}^{\′}\right)}^2+{\left({\stackrel{..}{z}}^{\text{\′}}\right)}^2}---\left(5\right)$

Wherein

With

Represent accelerometer output valve along x, y and z axes under carrier coordinate system.The output of course angle can be obtained by output or the gyrostatic output of magnetometer.Use magnetometer m=[m _x, m _y, m _z] ^TWhen asking for course angle, need to make the z axle of carrier coordinate system and navigation coordinate by spin matrix is the Z axis alignment, then asks for the navigation angle, and formula is as follows:

m′＝R′m (7)

${\mathrm{\φ}}_{\mathrm{mag}}=\arctan \left(\frac{m_y^{\′}}{m_x^{\′}}\right)---\left(8\right)$

M ' wherein _xAnd m ' _yX ' axle and the y ' axle component of expression earth magnetic field intensity component after the alignment.

Accelerometer measures obtains carrier at x, y, and the acceleration on the z axle obtains roll angle and the angle of pitch of carrier and the velocity information of carrier by acceleration.

Magnetometer survey obtains the magnetic field of the earth at x, y, and the magnetic field strength component on the z axle is obtained the course angle of carrier, is called the first course angle.

Gyroscope survey carrier angular velocity information calculates the roll angle, the angle of pitch and the second course angle that obtain carrier.

Merge the first course angle that the second course angle that gyroscope calculates and magnetometer calculate by complementary filter, obtain course angle.Certainly, also can not carry out complementary filter, directly use the first course angle or the second course angle.Certainly, also can utilize the GPS technology to obtain course angle.

The course angle of utilizing gyroscope to calculate is revised the output of magnetometer, and formula is as follows:

${\mathrm{\φ}}_k^{\mathrm{gyr}}={\mathrm{\φ}}_{k-1}^{\mathrm{gyr}}+{\mathrm{\ω}}_k\mathrm{dt}---\left(9\right)$

$\mathrm{\φ}=(1-W){\mathrm{\φ}}_k^{\mathrm{gyr}}+{\mathrm{W\φ}}_{\mathrm{mag}}---\left(10\right)$

Wherein

The course angle that expression is calculated by gyroscope, ω _kThe expression gyroscope is at the angular speed of k period, and W is the complementary weight of designed complementary filter, 0≤W≤1.

3) particle filter based on WLAN and MARG designs

The particle filter step:

(1) course angle and the velocity information of Kalman filter output are inputted as the state value of particle filter;

(2) initialization particle: adopt Gaussian Profile to come the probability density function of initialization particle, average is the target initial condition, and described initial condition comprises course angle and velocity information;

(3) prediction: utilize course angle and velocity information, and the WLAN positioning result, next step state information of particle filter target of prediction;

(4) granular Weights Computing and normalization: ask for the weight of each particle by measurement model and present measured value, when particle position during the closer to the current estimated state of target, the weight that particle obtains is larger;

(5) resample: produce new particle according to posterior probability density function, solve the particle degenerate problem.

The particle filter output position information, course angle information, velocity information.Positional information is as mobile subscriber's location estimation.

Concrete filtering method is as follows:

Particle filter is to adopt one group of particle collection that randomly draw from probability density function and subsidiary relevant weights to approach posterior probability density function:

$\mathrm{pr}\left(x_k\right|Z_{0:k})=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_k^i\mathrm{\δ}(x_k-x_k^i)---\left(11\right)$

X wherein _kThe expression target is at the state vector of moment k, z _0:kBe illustrated in constantly k+1 measured value sequence before,

Represent i particle or sample point,

Be its weight, N is population.The particle filter that adopts in the native system is divided into following four steps:

1) initialization

According to initial probability density function Pr (x ₀) N particle of generation

Pr (x wherein ₀) adopting Gaussian Profile, average is the target initial condition, this value can be set as required.Carry out first particle filter and need to carry out initialization, the particle that obtains behind the front filtering resampling of follow-up use.

2) prediction

In conjunction with MARG sensing data result, next step state information (x of particle filter target of prediction _K+1, y _K+1), formula is as follows:

$\left[\begin{array}{c}{x}_{k+1}\\ y_{k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& T_s\·\cos \left({\mathrm{\φ}}_k\right)\\ 0& 1& T_s\·\sin \left({\mathrm{\φ}}_k\right)\end{array}\right]\left[\begin{array}{c}{x}_k\\ y_k\\ v_k\end{array}\right]+\left[\begin{array}{cc}\frac{T_s^2}{2}& 0\\ 0& \frac{T_s^2}{2}\end{array}\right]\left[\begin{array}{c}{\mathrm{\η}}_x\\ {\mathrm{\η}}_y\end{array}\right]---\left(12\right)$

[x wherein _k, y _k] ^TBe the state vector of each particle, T _sThe time interval that represents the k-1 time WLAN location and the k time WLAN location, φ _kThe target that expression MARG sensing data process Kalman filtering obtains is around the anglec of rotation (being course angle) of z axle, v _kThe target velocity that obtains after expression MARG sensing data is processed, [η _x, η _y] ^TThe acceleration of expression target, with the Gaussian noise simulation of zero-mean, variance can be estimated by the MARG sensing data.

3) weight calculation and normalization

The weight of particle is asked for by measurement model and current measured value:

${\mathrm{\ω}}_{k+1}^i=\mathrm{pr}\left[Z_{k+1}\right|x_k^i]=\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp [-\frac{\left|\right|x_k^z-x_k^i\left|\right|}{{2\mathrm{\σ}}^2}]---\left(13\right)$

${\mathrm{\ω}}_{k+1}^i=\frac{{\mathrm{\ω}}_{k+1}^i}{\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_{k+1}^j}---\left(14\right)$

Z wherein _kThe RSS of the current measurement of expression target,

The current state information (position, course angle) of expression target,

Represent i particle in the state information of moment k, σ represents to measure noise variance, selects according to the variance that RSS in the reality floats.Formula (13) expression is when particle position during the closer to the current estimated position of target, and the weight that particle obtains is larger, thereby obtains accurately posterior probability distribution.

4) resample

Resampling is the key of particle filter, according to probability density function Pr (x _k| z _k) N new particle of generation

Solve the particle degenerate problem, a kind of method for resampling is as follows:

$\mathrm{pr}\left(x_k\right|z_k)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_k^i\mathrm{\δ}(x_k-x_k^i)---\left(15\right)$

$\mathrm{pr}(x_k^{i*}=x_k^i)={\mathrm{\ω}}_k^i---\left(16\right)$

4) Kalman filtering based on WLAN and MARG designs

The serious accuracy that relies on course angle φ of the quality of particle filter, in order further to eliminate because gyroscope exists accumulated error and magnetometer to be subject to the interference of local magnetic field on every side, the course angle φ error that obtains after causing the MARG data to be processed, in the present embodiment, a Kalman filter also can be provided, attitude information (the positional information that comprises particle filter output of utilizing particle filter to obtain, bearer rate information, course angle information etc.) revise course angle φ, thereby obtain reliable and stable course angle information.

The step of Kalman filtering:

(1) roll angle, the angle of pitch and the course angle of the acquisition of MARG transducer, and bearer rate information are as the input of Kalman filtering state value.

(2) carry out next step prediction of state of Kalman filtering;

(3) calculate the predicated error variance matrix;

(4) calculation of filtered gain matrix;

(5) state estimation;

(6) estimation error variance is calculated.

Kalman filtering algorithm as shown in the formula:

$\left\{\begin{array}{c}{\mathrm{\φ}}_k^{-}={\mathrm{\φ}}_{k-1}-\stackrel{.}{\mathrm{\φ}}\·\mathrm{\ΔT}\\ P_k^{-}=Q+P_{k-1}\\ K_k=P_k^{-}\·{[P_k^{-}+R]}^{-1}\\ {\mathrm{\φ}}_k={\mathrm{\φ}}_k^{-}+K_k[{\mathrm{\φ}}_{\mathrm{PF}}-{\mathrm{\φ}}_k^{-}]\\ P_k=(1-K_k)P_k^{-}\end{array}\right.---\left(17\right)$

Wherein

The angular speed of expression gyroscope output, φ _K-1The course angle of predicting during expression moment k-1, Δ T represents the measuring intervals of TIME of MARG transducer, Q and R represent respectively process noise and measure the covariance matrix of noise, K _kBe the Kalman filter gain, And P _kThe expression varivance matrix, φ _PFCourse angle for the particle filter estimation.

The WLAN/MARG integrated positioning system that the based on data that the embodiment of the invention proposes merges, this system utilizes the MARG transducer to obtain the information such as speed, attitude of moving target, improve the WLAN positioning accuracy by data anastomosing algorithms such as complementary filter, Kalman filtering and particle filters, realized a low-cost and high-precision WLAN/MARG integrated positioning system.

One of ordinary skill in the art will appreciate that all or part of step in the said method can come the instruction related hardware to finish by program, described program can be stored in the computer-readable recording medium, such as read-only memory, disk or CD etc.Alternatively, all or part of step of above-described embodiment also can realize with one or more integrated circuits.Correspondingly, each the module/unit in above-described embodiment can adopt the form of hardware to realize, also can adopt the form of software function module to realize.The present invention is not restricted to the combination of the hardware and software of any particular form.

Claims (16)

1. a localization method is characterized in that, comprising:

Access point based on WLAN (wireless local area network) positions, and obtains the initial estimated location of subscriber equipment;

Obtain course angle and the velocity information of described subscriber equipment;

According to described course angle and velocity information described initial estimated location is revised, obtained final position information.

2. the method for claim 1 is characterized in that, described access point based on WLAN (wireless local area network) positions and comprises:

Choose reference point, measure signal strength signal intensity from each access point in each reference point, the sign/position of the position of described reference point, described signal strength signal intensity and corresponding access point is stored in the database; The signal strength signal intensity of each access point around subscriber equipment to be positioned is measured is searched database and is obtained corresponding reference point set, mates the initial estimated location of determining subscriber equipment with described reference point set.

3. method as claimed in claim 2 is characterized in that, described and reference point set is mated the initial estimated location of determining described subscriber equipment and comprised:

Select the reference point of the Euclidean distance minimum of m received signal strength, use a described m reference point the position linear weighted function and as the initial estimated location of described subscriber equipment, described m is more than or equal to 1.

4. the method for claim 1 is characterized in that, described course angle and the velocity information of obtaining described subscriber equipment comprises:

Obtain described course angle and velocity information according to the metrical information of MARG transducer.

5. method as claimed in claim 4 is characterized in that, described metrical information according to the MARG transducer is obtained described course angle and comprised:

Metrical information according to the magnetometer of described MARG transducer is obtained the first course angle φ _Mag, obtain the second course angle according to the gyrostatic metrical information of described MARG transducer Obtain the course angle φ of described subscriber equipment according to described the first course angle and the second course angle:

$\mathrm{\φ}=(1-W){\mathrm{\φ}}_k^{\mathrm{gyr}}+{\mathrm{W\φ}}_{\mathrm{mag}}$

Wherein, described W is the weighted value of presetting, 0≤W≤1.

6. such as claim 4 or 5 described methods, it is characterized in that,

Described method also comprises, obtains roll angle, the angle of pitch of described subscriber equipment according to the metrical information of described MARG transducer;

Describedly according to described course angle and velocity information described initial estimated location correction is comprised:

With the input of described roll angle, the angle of pitch, course angle and the velocity information of obtaining according to the metrical information of described MARG transducer as Kalman filter, carry out Kalman filtering, export new course angle and velocity information;

With the input as particle filter of the course angle of described Kalman filter output and velocity information and described initial estimated location, carry out particle filter, output position information, course angle and velocity information, with the positional information of output as the final position information of described subscriber equipment.

7. method as claimed in claim 6 is characterized in that, the described Kalman filtering of carrying out comprises:

Carry out the state one-step prediction of Kalman filtering,

Calculate the predicated error variance matrix

The calculation of filtered gain matrix $K_k=P_k^{-}\·{[P_k^{-}+R]}^{-1};$

Carry out state estimation ${\mathrm{\φ}}_k={\mathrm{\φ}}_k^{-}+K_k[{\mathrm{\φ}}_{\mathrm{PF}}-{\mathrm{\φ}}_k^{-}];$

Calculate estimation error variance

Wherein, described

The angular speed that represents the gyroscope output of described MARG transducer, φ _K-1The course angle of obtaining according to described MARG transducer during expression moment k-1, Δ T represents the measuring intervals of TIME of described MARG transducer, Q and R represent respectively process noise and measure the covariance matrix of noise, K _kBe the Kalman filter gain,

And P _kThe expression varivance matrix, φ _PFThe course angle of particle filter output when locating for the last time, first φ during Kalman filtering _PFBe designated value.

8. method as claimed in claim 6 is characterized in that, the described particle filter that carries out comprises:

When carrying out particle filter first, need the initialization particle, adopt Gaussian Profile to come the probability density function of initialization particle;

According to described course angle and velocity information, and described initial estimated location, predict next step state information of described subscriber equipment:

$\left[\begin{array}{c}{x}_{k+1}\\ y_{k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& T_s\·\cos \left({\mathrm{\φ}}_k\right)\\ 0& 1& T_s\·\sin \left({\mathrm{\φ}}_k\right)\end{array}\right]\left[\begin{array}{c}{x}_k\\ y_k\\ v_k\end{array}\right]+\left[\begin{array}{cc}\frac{T_s^2}{2}& 0\\ 0& \frac{T_s^2}{2}\end{array}\right]\left[\begin{array}{c}{\mathrm{\η}}_x\\ {\mathrm{\η}}_y\end{array}\right]$

Calculate weight and the normalization of each particle, as follows:

${\mathrm{\ω}}_{k+1}^i=\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp [-\frac{\left|\right|x_k^z-x_k^i\left|\right|}{{2\mathrm{\σ}}^2}]$

${\mathrm{\ω}}_{k+1}^i=\frac{{\mathrm{\ω}}_{k+1}^i}{\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_{k+1}^j}$

Carry out particle and resample, as the particle of particle filter next time;

Wherein, described [x _k, y _k] ^TBe the state vector of each particle, T _sThe expression last time is based on location and this time interval based on the location of the access point of WLAN (wireless local area network) of the access point of WLAN (wireless local area network), φ _kRepresent described course angle, v _kRepresent described velocity information, [η _x, η _y] ^TThe expression acceleration, with the simulation of the Gaussian noise of zero-mean, variance is by the metrical information estimation of described MARG transducer,

For inputting the state value of described particle filter,

Represent i particle at the state value of moment k, σ represents the noise variance of signal strength measurement.

9. a navigation system is characterized in that, comprising:

The WLAN locating module is used for positioning based on the access point of WLAN (wireless local area network), obtains the initial estimated location of subscriber equipment;

The sensor localization module is for course angle and the velocity information of obtaining described subscriber equipment;

Fusion Module is used for according to described course angle and velocity information described initial estimated location being revised, and obtains final position information.

10. system as claimed in claim 9 is characterized in that, described WLAN locating module comprises:

Database is used for being stored in the signal strength signal intensity from each access point that each reference point is measured, the position of described reference point and the sign/position of corresponding access point;

The RSS measuring unit is used for measuring subscriber equipment to be positioned and measures the signal strength signal intensity of each access point on every side;

Positioning unit is used for the signal strength signal intensity according to described RSS measuring unit measurement, searches database and obtains corresponding reference point set, mates the initial estimated location of determining subscriber equipment with described reference point set.

11. system as claimed in claim 10 is characterized in that, described positioning unit and reference point set are mated the initial estimated location of determining described subscriber equipment and are comprised:

Described positioning unit is selected the reference point of the Euclidean distance minimum of m received signal strength, use a described m reference point the position linear weighted function and as the initial estimated location of described subscriber equipment, described m is more than or equal to 1.

12. system as claimed in claim 9 is characterized in that, described sensor localization module comprises: MARG transducer and data processing unit, wherein:

Described MARG transducer is used for, and described subscriber equipment is measured, and obtains metrical information;

Described data processing unit is used for, and obtains described course angle and velocity information according to the metrical information of MARG transducer.

13. system as claimed in claim 12 is characterized in that, described data processing unit obtains described course angle according to the metrical information of MARG transducer and comprises:

Described data processing unit obtains the first course angle φ according to the metrical information of the magnetometer of described MARG transducer _Mag, obtain the second course angle according to the gyrostatic metrical information of described MARG transducer Obtain the course angle φ of described subscriber equipment according to described the first course angle and the second course angle:

$\mathrm{\φ}=(1-W){\mathrm{\φ}}_k^{\mathrm{gyr}}+{\mathrm{W\φ}}_{\mathrm{mag}}$

Wherein, described W is the weighted value of presetting, 0≤W≤1.

14. such as claim 12 or 13 described systems, it is characterized in that described Fusion Module comprises: Kalman filter and particle filter, wherein:

Described data processing unit also is used for, and obtains roll angle, the angle of pitch of described subscriber equipment according to the metrical information of described MARG transducer;

Described Kalman filter is used for, described roll angle, the angle of pitch, course angle and the velocity information of obtaining according to the metrical information of described MARG transducer inputted as the state value of Kalman filter, carry out Kalman filtering, export new course angle and velocity information;

Described particle filter is used for, course angle and velocity information with described Kalman filter output, and described initial estimated location is inputted as state value, carry out particle filter, output position information, course angle and velocity information, with the positional information of output as the final position information of described subscriber equipment.

15. system as claimed in claim 14 is characterized in that, described Kalman filter is carried out Kalman filtering and is comprised:

Carry out the state one-step prediction of Kalman filtering,

Calculate the predicated error variance matrix

The calculation of filtered gain matrix $K_k=P_k^{-}\·{[P_k^{-}+R]}^{-1};$

Carry out state estimation ${\mathrm{\φ}}_k={\mathrm{\φ}}_k^{-}+K_k[{\mathrm{\φ}}_{\mathrm{PF}}-{\mathrm{\φ}}_k^{-}];$

Calculate estimation error variance

Wherein, described

The angular speed that represents the gyroscope output of described MARG transducer, φ _K-1The course angle of obtaining according to described MARG transducer during expression moment k-1, Δ T represents the measuring intervals of TIME of described MARG transducer, Q and R represent respectively process noise and measure the covariance matrix of noise, K _kBe the Kalman filter gain,

And P _kThe expression varivance matrix, φ _PFThe course angle of particle filter output when locating for the last time, first φ during Kalman filtering _PFBe designated value.

16. system as claimed in claim 14 is characterized in that, the described particle filter that carries out comprises:

The initialization particle adopts Gaussian Profile to come the probability density function of initialization particle;

According to described course angle and velocity information, and described initial estimated location, predict next step state information of described subscriber equipment:

$\left[\begin{array}{c}{x}_{k+1}\\ y_{k+1}\end{array}\right]=\left[\begin{array}{ccc}1& 0& T_s\·\cos \left({\mathrm{\φ}}_k\right)\\ 0& 1& T_s\·\sin \left({\mathrm{\φ}}_k\right)\end{array}\right]\left[\begin{array}{c}{x}_k\\ y_k\\ v_k\end{array}\right]+\left[\begin{array}{cc}\frac{T_s^2}{2}& 0\\ 0& \frac{T_s^2}{2}\end{array}\right]\left[\begin{array}{c}{\mathrm{\η}}_x\\ {\mathrm{\η}}_y\end{array}\right]$

Calculate weight and the normalization of each particle, as follows:

${\mathrm{\ω}}_{k+1}^i=\frac{1}{\sqrt{2\mathrm{\π}}\mathrm{\σ}}\exp [-\frac{\left|\right|x_k^z-x_k^i\left|\right|}{{2\mathrm{\σ}}^2}]$

${\mathrm{\ω}}_{k+1}^i=\frac{{\mathrm{\ω}}_{k+1}^i}{\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\ω}}_{k+1}^j}$

Carry out particle and resample, as the particle of particle filter next time;

Wherein, described [x _k, y _k] ^TBe the state vector of each particle, T _sThe expression last time is based on location and this time interval based on the location of the access point of WLAN (wireless local area network) of the access point of WLAN (wireless local area network), φ _kRepresent described course angle, v _kRepresent described velocity information, [η _x, η _y] ^TThe expression acceleration, with the simulation of the Gaussian noise of zero-mean, variance is by the metrical information estimation of described MARG transducer, Be the state value of the described particle filter of current input,

Represent i particle at the state value of moment k, σ represents the noise variance of signal strength measurement.

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