CN106658704A - Positioning method and system of starting point of indoor positioning - Google Patents
Positioning method and system of starting point of indoor positioning Download PDFInfo
- Publication number
- CN106658704A CN106658704A CN201611048962.XA CN201611048962A CN106658704A CN 106658704 A CN106658704 A CN 106658704A CN 201611048962 A CN201611048962 A CN 201611048962A CN 106658704 A CN106658704 A CN 106658704A
- Authority
- CN
- China
- Prior art keywords
- rss
- probability
- print data
- finger print
- starting point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 239000013598 vector Substances 0.000 claims description 27
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000004807 localization Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 description 6
- 238000012549 training Methods 0.000 description 6
- 241001269238 Data Species 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- ZFXYFBGIUFBOJW-UHFFFAOYSA-N theophylline Chemical compound O=C1N(C)C(=O)N(C)C2=C1NC=N2 ZFXYFBGIUFBOJW-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Probability & Statistics with Applications (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Collating Specific Patterns (AREA)
Abstract
The invention provides a positioning method and system of a starting point of indoor positioning. The method comprises the following steps: acquiring finger position of a smart terminal location, establishing a starting point fingerprint database corresponding to the fingerprint data and location; acquiring the current fingerprint data of the smart terminal, and acquiring the position probability of each position of the smart terminal in the starting point fingerprint database according to the fingerprint data; acquiring the maximum one in the position probability, wherein the corresponding location with the maximum position probability is the starting point position of the smart terminal. The starting point fingerprint database is established, the possible starting point position of the user is acquired through the fingerprint data and the probability computation, and then the starting position of the user is determined by computing the maximum one in the position probability, thereby determining the starting point position of the user more accurately, and providing better data support for the PDR; the indoor positioning can be performed faster and more accurately, and the usability and the accuracy of the indoor positioning are improved.
Description
Technical field
The present invention relates to communication technical field, more particularly to a kind of start position of indoor positioning localization method and be
System.
Background technology
With the fast development of smart mobile phone and mobile Internet, location Based service has attracted increasing pass
Note.In real time positioning has become the basic fundamental of multiple high-level applications such as traffic, business, logistics, individual service.In outdoor
In the case of, GLONASS has been obtained there is provided a good positioning service, such as global positioning system (GPS).So
And, indoors in environment, due to signal fadeout and multipath effect, GPS is unable to reach suitable precision.
Current intelligent terminal collection such as smart mobile phone etc. is into many built-in sensors, such as direction sensor, acceleration
Sensor, magnetometer etc..PDR (Pedestrian Dead Reckoning) location technology based on mobile phone sensor data,
Increasing concern is obtained.PDR location technologies, start positioning when, need an accurate reference point locations, as
Initial point.At present main PDR solutions, it is necessary to provide the initial point position of a determination, just can be extrapolating pedestrian
Movement locus.This problem greatly constrains the popularization and application of PDR location technologies.
Therefore, how a kind of more user-friendly indoor orientation method and system are provided, are determined more accurately
Start position in indoor positioning, to improve the ease for use and accuracy of indoor positioning, becomes the problem of this area urgent need to resolve.
The content of the invention
It is an object of the invention to provide a kind of more user-friendly indoor orientation method and system, more accurately
The start position in indoor positioning is determined, to improve the ease for use and accuracy of indoor positioning.
The purpose of the present invention is achieved through the following technical solutions：
A kind of localization method of the start position of indoor positioning, including：
The finger print data of collection intelligent terminal position, sets up finger print data starting point fingerprint corresponding with position
Storehouse；
The current finger print data of intelligent terminal is obtained, and according to finger print data acquisition intelligent terminal in starting point fingerprint base
The location probability of each position；
Maximum in the location probability one is obtained, the maximum corresponding position of the location probability is whole for intelligence
The start position at end.
Preferably, the finger print data includes：RSS vector sum coordinate values；The finger of the collection intelligent terminal position
Line data, specifically include the step of set up finger print data corresponding with position starting point fingerprint base：
Multiple datum nodes are set in positioning region；
RSS vector sum corresponding coordinate value of the collection intelligent terminal in datum node；
It is vectorial according to the RSS of each datum node, calculate the RSS averages and mean variance of each datum node, and by its
It is stored in fingerprint database as the finger print data of the datum node.
Preferably, the meter of the location probability of each position of the intelligent terminal in starting point fingerprint base is obtained according to finger print data
Calculate step to specifically include：Wherein, RSS averages are respectively with mean variance：μ=E (RSSi), σ=E (| RSSi- μ |), wherein, i is
Natural number, u is the average of RSS, and σ is the mean variance of RSS, and the RSS values size of each datum node is high in reference point locations
This normal distribution, i.e.,
The probability of the RSS values is：
Wherein PrssFor the probability of the RSS values of datum node, x is current
The x-axis coordinate value of datum node, u is the average of RSS, and σ is the mean variance of RSS.
Preferably, the calculation procedure is further included：According toCalculate probability, wherein m
Represent the number of the RSS vectors of collection, Sj={ RSSj1, RSSj2..., RSSjnA RSS vector is represented, n is indoor arrangement
WAP quantity；It is exactly the current position of user to obtain the maximum starting point of probable value：SP=argmaxp (S | SPn)。
Preferably, wherein, in the calculating of the probability of RSS values, only calculate signal strength signal intensity -30dBm to -90dBm RSS values
Distribution probability Prss。
The present invention discloses a kind of alignment system of the start position of indoor positioning, including：
Data acquisition module, for gathering the finger print data of intelligent terminal position, sets up finger print data in place with institute
Put corresponding starting point fingerprint base；
Acquisition module, the finger print data current for obtaining intelligent terminal, and existed according to finger print data acquisition intelligent terminal
The location probability of each position in starting point fingerprint base；
Processing module, for obtaining the location probability in maximum one, the maximum corresponding institute of the location probability
The start position of intelligent terminal is set in place.
Preferably, the finger print data includes：RSS vector sum coordinate values；The data acquisition module specifically for：
Multiple datum nodes are set in positioning region；
RSS vector sum corresponding coordinate value of the collection intelligent terminal in datum node；
It is vectorial according to the RSS of each datum node, calculate the RSS averages and mean variance of each datum node, and by its
It is stored in fingerprint database as the finger print data of the datum node.
Preferably, the acquisition module specifically for：Wherein, RSS averages are respectively with mean variance：μ=E (RSSi), σ
=E (| RSSi- μ |), wherein, i is natural number, u for RSS average, σ for RSS mean variance, the RSS of each datum node
Value size is Gauss normal distribution in reference point locations, i.e.,
The probability of the RSS values is：
Wherein PrssFor the probability of the RSS values of datum node, x is current
The x-axis coordinate value of datum node, u is the average of RSS, and σ is the mean variance of RSS.
Preferably, the acquisition module specifically for：According toCalculate probability, wherein m tables
Show the number of the RSS vectors of collection, Sj={ RSSj1, RSSj2..., RSSjnA RSS vector is represented, n is indoor arrangement
The quantity of WAP；It is exactly the current position of user to obtain the maximum starting point of probable value：SP=argmaxp (S | SPn)。
Preferably, wherein, in the calculating of the probability of RSS values, only calculate signal strength signal intensity -30dBm to -90dBm RSS values
Distribution probability Prss。
The indoor orientation method of the present invention is due to including the finger print data of collection intelligent terminal position, setting up fingerprint number
According to starting point fingerprint base corresponding with position；The current finger print data of intelligent terminal is obtained, and intelligence is obtained according to finger print data
The location probability of each position of the energy terminal in starting point fingerprint base；Obtain maximum in the location probability one, the position
The corresponding position of maximum probability is the start position of intelligent terminal.Adopt in this way, initially set up starting point fingerprint
Storehouse, by finger print data, and through probability calculation obtain user possible start position, then by calculate location probability in most
A big start position to determine user, is PDR so as to the start position of determination user for more preparing
(Pedestrian Dead Reckoning, pedestrian's dead reckoning) provides more preferable data and supports, so as to faster more accurately
Indoor positioning is carried out, the ease for use and accuracy of indoor positioning is improved.
Description of the drawings
Fig. 1 is a kind of flow chart of indoor orientation method of the embodiment of the present invention；
Fig. 2 is the schematic diagram of the finger print data record of the embodiment of the present invention；
Fig. 3 is the schematic diagram of the starting point fingerprint base of the embodiment of the present invention；
Fig. 4 is a kind of schematic diagram of indoor locating system of the embodiment of the present invention.
Specific embodiment
Although operations to be described as flow chart the process of order, many of which operation can by concurrently,
Concomitantly or while implement.The order of operations can be rearranged.Processing when its operations are completed to be terminated,
It is also possible to have the additional step being not included in accompanying drawing.Process can correspond to method, function, code, subroutine, son
Program etc..
Computer equipment includes user equipment and the network equipment.Wherein, user equipment or client include but is not limited to electricity
Brain, smart mobile phone, PDA etc.；The network equipment includes but is not limited to single network server, the service of multiple webservers composition
Device group or the cloud being made up of a large amount of computers or the webserver based on cloud computing.Computer equipment can isolated operation realizing
The present invention, also can access network and by with network in other computer equipments interactive operation realizing the present invention.Calculate
Network residing for machine equipment includes but is not limited to internet, wide area network, Metropolitan Area Network (MAN), LAN, VPN etc..
May have been used term " first ", " second " etc. here to describe unit, but these units should not
When limited by these terms, it is used for the purpose of making a distinction a unit and another unit using these terms.Here institute
The term "and/or" for using includes any and all combination of one of them or more listed associated items.When one
Unit is referred to as " connection " or during " coupled " to another unit, and it can be connected or coupled to another unit, or
There may be temporary location.
Term used herein above is not intended to limit exemplary embodiment just for the sake of description specific embodiment.Unless
Context clearly refers else, and singulative " one " otherwise used herein above, " one " also attempt to include plural number.Should also
When being understood by, term " including " used herein above and/or "comprising" specify stated feature, integer, step, operation,
The presence of unit and/or component, and do not preclude the presence or addition of one or more other features, integer, step, operation, unit,
Component and/or its combination.
Below in conjunction with the accompanying drawings the invention will be further described with preferred embodiment.
Embodiment one
As shown in figure 1, a kind of localization method of the start position of indoor positioning disclosed in the present embodiment, including：
S101, the finger print data of collection intelligent terminal position, set up finger print data starting point corresponding with position
Fingerprint base；
The current finger print data of S102, acquisition intelligent terminal, and intelligent terminal is obtained in starting point fingerprint according to finger print data
The location probability of each position in storehouse；
S103, obtain maximum in the location probability one, the maximum corresponding position of the location probability is
The start position of intelligent terminal.
The localization method of the start position of the indoor positioning of the present invention is due to including the finger of collection intelligent terminal position
Line data, set up finger print data starting point fingerprint base corresponding with position；Obtain the current finger print data of intelligent terminal, and root
The location probability of each position of the intelligent terminal in starting point fingerprint base is obtained according to finger print data；Obtain in the location probability most
Big one, the maximum corresponding position of the location probability is the start position of intelligent terminal.Adopt in this way, it is first
Starting point fingerprint base is first set up, by finger print data, and the possible start position of user is obtained through probability calculation, then by meter
A start position to determine user maximum in location probability is calculated, so as to the start position of determination user for more preparing,
More preferable data are provided for PDR (Pedestrian Dead Reckoning, pedestrian's dead reckoning) to support, so as to faster more
Indoor positioning is accurately carried out, the ease for use and accuracy of indoor positioning is improved.The institute by way of above-mentioned utilization WiFi fingerprints
The start position obtained, you can be supplied to PDR methods with as a reference point.Such that it is able to allow PDR technologies to be referred to according to this
Point extrapolates the movement locus of user, and PDR is step number, the step that (Pedestrian Dead Reckoning) walks to pedestrian
Long, direction measures and counts, and extrapolates pedestrian's run trace, and the information such as position.
According to one of example, the finger print data includes：RSS vector sum coordinate values；The collection intelligent terminal institute
In the finger print data of position, specifically include the step of set up finger print data corresponding with position starting point fingerprint base：
Multiple datum nodes are set in positioning region；
RSS vector sum corresponding coordinate value of the collection intelligent terminal in datum node；
It is vectorial according to the RSS of each datum node, calculate the RSS averages and mean variance of each datum node, and by its
It is stored in fingerprint database as the finger print data of the datum node.
Database thus can be more accurately set up, convenient positioning improves the accuracy of positioning.
According to one of example, the position of each position of the intelligent terminal in starting point fingerprint base is obtained according to finger print data
The calculation procedure for putting probability is specifically included：Wherein, RSS averages are respectively with mean variance：μ=E (RSSi), σ=E (| RSSi-μ
|), wherein, i is natural number, and u is the average of RSS, and σ is the mean variance of RSS, and the RSS values size of each datum node is in reference
Point position is Gauss normal distribution, i.e.,
The probability of the RSS values is：
Wherein PrssFor the probability of the RSS values of datum node, x is current
The x-axis coordinate value of datum node, u is the average of RSS, and σ is the mean variance of RSS.
According to one of example, the calculation procedure is further included：According to
Probability is calculated, wherein m represents the number of the RSS vectors of collection, Sj={ RSSj1,
RSSj2..., RSSjnA RSS vector is represented, n is the quantity of the WAP of indoor arrangement；Obtain probable value maximum
Starting point is exactly the current position of user：SP=argmaxp (S | SPn)。
Calculated with company as procedure described above, it is possible to calculate the probability of each possible position, so as to filter out most
Maximum probability position, and user positioned accordingly.
According to one of example, wherein, in the calculating of the probability of RSS values, only calculate signal strength signal intensity -30dBm to -
The distribution probability P of the RSS values of 90dBmrss.In view of the validity of signal strength values, we are only recorded from -30dBm to -90dBm
RSS values distribution probability.
In daily life, the time of people's overwhelming majority all stays indoors, and is in interior in most cases,
The mobile phone of people is all idle.It is desirable that mobile phone is under these idle states, fingerprint sampling can be used for, while
Our other behaviors are not interfered with yet.The RSS vectors obtained in a certain reference point are exactly RSSi(RSSi,1,RSSi,2,
RSSi,3,RSSi,4).And point coordinates is referred to for Li(xi,yi), RSSiWith LiOne-to-one relationship, the as fingerprint of the reference point,
The interrecord structure of finger print data is as shown in Figure 2；
User Defined node represents the position that user regular can stay for some time in daily life, for example, handle official business
Table, Tea Room etc..Because User Defined node is the point that some meetings Jing is often stopped, so when sample collector rests on user
At self-defined node positions, substantial amounts of fingerprint training dataset can be gathered.Fingerprint training dataset, can by certain process
To generate starting point training fingerprint base.The form of starting point training fingerprint base is as shown in Figure 3；
In upper source of graph training fingerprint base, the MAC Address of smart mobile phone represents different sample collectors, because difference is adopted
The different mobile phone of sample librarian use, MAC Address is the unique mark as equipment identification.What every sample collector was likely to occur rises
Point constitutes one and plays point set, it is possible to see that each MAC Address correspond to one point set in starting point training storehouse.
Calculated by above-mentioned method afterwards.
In the present embodiment, after being positioned to start position using such scheme, next can be according to starting point position
Putting carries out indoor positioning, and concrete scheme is as follows.
Wide variety of indoor locating system can allow for as all of mobile terminal in the range of large-scale city
Accurate positioning service is provided, when collection fingerprint database mobile device it is inconsistent with the mobile device model of tuning on-line
When, the positioning precision of alignment system will be affected by certain.However, because the equipment for gathering RSSI fingerprint databases always has
Limit, it is impossible to which the mobile phone for each model sets up a RSSI fingerprint database, therefore, it is necessary to take certain measure to delay
Impact of the solution equipment difference to positioning precision.
Two classes can be divided into based on the WiFi location technologies of location fingerprint：Deterministic Methods, the method based on probability.It is determined that
Property method in, using RSSI receive signal mean value as fingerprint base primitive.
In the off-line data collecting stage, if mobile terminal certain reference point (x, y) through a period of time sampled acquisition
The RSSI sample data sets for arriving are vector RSSI={ rssi1, rssi2, rssi3...rssin, by the average of sample data set and
Positional information is stored in database the finger print data as the point and is designated as (x, y, rssi1, rssi2, rssi3, rssi4).When all
After the RSSI data acquisitions of reference point are complete, a complete fingerprint database can be obtained.
In real-time positioning stage, the RSSI data that mobile terminal is collected are vector RSSI={ rssi1, rssi2, rssi3,
rssi4, the RSSI data for obtaining in real time are matched with the RSSI samples in fingerprint database, estimate the position of user.Most
It is a kind of matching algorithm the simplest that neighbour occupies method (Nearest Neighbor in Signal Space).It is by calculating
In real time RSSI data with fingerprint database the signal strength signal intensity of the RSSI sample datas of a certain reference point apart from d (vectorial RSSI,
RSSI), shown in its circular such as formula (3-1).The minimum reference point conduct of nearest-neighbors method selection signal intensity distance
Final estimated location.
Wherein, N is wireless aps number, and when parameter p=1, calculating is manhatton distance, is Euclidean distance during p=2, is led to
Often we select Euclidean distance to calculate the distance between signal strength signal intensity.The position that nearest-neighbors method is obtained is necessarily in fingerprint database
In the reference point locations that existed.K-nearest neighbor (K-NNSS) is a kind of modified version of nearest-neighbors method, and it is no longer simple
Use point closest in signal space as location estimation, but using the mean value of closest several sampled points
To estimate the position of user.
It is that one kind is based on Bayesian location algorithm based on the method for probability.First, in off-line data collecting rank
Section, have recorded location fingerprint set (L, the RSSI)={ (L of n position1,RSSI1),(L2,RSSI2), (L3,RSSI3)...
(LN,RSSIN), in real-time positioning stage, the RSSI sample datas arrived in certain station acquisition are RSSI={ rssi1, rssi2,
rssi3, rssi4}.It is exactly that real-time RSSI is extrapolated in fingerprint database by Bayes' theorem based on the method for probability
The posterior probability of known location, is designated as p (LiShu RSSI).Concrete reasoning process is as follows：
Wherein, p (LiShu RSSI) obtain the probability of real-time sample data RSSI in certain position.p(Li) it is position LiElder generation
Test probability, it is generally the case that task user be likely to occur at an arbitrary position, so it can regard one as be uniformly distributed, and p
(RSSI Shu Lk) for so position can be regarded as a constant.In positioning stage, by calculated maximum a posteriori probability
Position as user location estimation.Therefore the calculating process of positioning can simplify such as formula (3-3)：
argmax[p(LiShu RSSI)]=argmax [p (RSSI Shu Li)] (3-3)
Signal between different AP may be considered it is independent incoherent, therefore, calculating can be reduced toIf be modeled to RSSI signals with Gauss model, formula (3- can be obtained
4)。
Wherein, u and σ represent the mean value and standard deviation of signal intensity samples data.
But the Orientation and Matching Algorithm of both types itself does not all consider asking for RSSI differentiation caused by equipment difference
Topic.Haeberlen proposes in the literature a kind of manual synchronizing method based on linear transformation, and the method adopts the side of manual synchronizing
Method obtains the Equation of Linear Transformation between distinct device, but the equipment in the face of hundreds and thousands of species diversity newly sets with what is continued to bring out
It is standby, it is unpractical using the method for manual synchronizing.
Set forth herein a kind of location algorithm based on RSSI differences.Although in the same place in same place, different mobile phones
The size of the RSSI sample datas for receiving is variant, but the RSSI differences between each AP for receiving of each mobile phone are but similar
's.Thus, it is supposed that the RSSI data that in real time positioning stage is collected are RSSI={ rssi1, rssi2, rssi3...rssin, choosing
Maximum of which AP signal strength values are selected as reference, rssi is designated asmax, then sequence of differences RSSI={ rssi can be obtained1-
rssimax, rssi2-rssimax, rssi3-rssimax...rssin-rssimax, it is abbreviated as RSSI={ Δ rssi1, Δ rssi2,
Δrssi3...Δrssin}.In the same manner, same process is made to the signal strength signal intensity sequence in fingerprint database.If fingerprint database
The RSSI sequences of middle reference point L are RSSI={ rssi1, rssi2, rssi3...rssin, maximum of which AP signal strength values
It is designated as rssimax, after process, its sequence of differences is RSSI={ rssi1-rssimax, rssi2-rssimax, rssi3-
rssimax...rssin-rssimax}={ Δ rssi1, Δ rssi2, Δ rssi3...Δrssin}.In positioning stage, by comparing
The sequence of differences of the RSSI of location fingerprint database and online real time collecting, carries out location estimation.For Deterministic Methods, logarithm
According to each reference point locations RSSI sequence in storehouse signal strength signal intensity distance calculated by formula (3-5), chosen distance is minimum
The mean value of one reference point or several reference points is used as location estimation.
For Gauss model method, each reference point locations RSSI sequence in database is calculated by formula (3-6)
The mean value of the similarity degree of signal strength signal intensity, a maximum reference point of select probability or several reference points is used as location estimation.
By this impact for alleviating equipment difference based on the location matches algorithm of RSSI differences to positioning precision.
According to one of embodiment of the invention, as shown in figure 4, the present embodiment discloses a kind of start position of indoor positioning
Alignment system, including：
Data acquisition module 401, for gathering the finger print data of intelligent terminal position, sets up finger print data and is located
The corresponding starting point fingerprint base in position；
Acquisition module 402, the finger print data current for obtaining intelligent terminal, and intelligent terminal is obtained according to finger print data
The location probability of each position in starting point fingerprint base；
Processing module 403, for obtaining the location probability in maximum one, maximum corresponding of the location probability
Position is the start position of intelligent terminal.
Adopt in this way, initially set up starting point fingerprint base, by finger print data, and obtain user's through probability calculation
Possible start position, then by a start position to determine user maximum in calculating location probability, so as to more accurate
The start position of standby determination user, is that PDR (Pedestrian Dead Reckoning, pedestrian's dead reckoning) is provided more
Good data are supported, so as to faster more accurately carry out indoor positioning, improve the ease for use and accuracy of indoor positioning.
According to one of example, the finger print data includes：RSS vector sum coordinate values；The data acquisition module tool
Body is used for：
Multiple datum nodes are set in positioning region；
RSS vector sum corresponding coordinate value of the collection intelligent terminal in datum node；
It is vectorial according to the RSS of each datum node, calculate the RSS averages and mean variance of each datum node, and by its
It is stored in fingerprint database as the finger print data of the datum node.
Database thus can be more accurately set up, convenient positioning improves the accuracy of positioning.
According to one of example, the acquisition module specifically for：Wherein, RSS averages are respectively with mean variance：μ
=E (RSSi), σ=E (| RSSi- μ |), wherein, i is natural number, and u is the average of RSS, and σ is the mean variance of RSS, each reference
The RSS values size of node is Gauss normal distribution in reference point locations, i.e.,
The probability of the RSS values is：
Wherein PrssFor the probability of the RSS values of datum node, x is current
The x-axis coordinate value of datum node, u is the average of RSS, and σ is the mean variance of RSS.
According to one of example, the acquisition module specifically for：According toCalculate general
Rate, wherein m represent the number of the RSS vectors of collection, Sj={ RSSj1, RSSj2..., RSSjnA RSS vector is represented, n is
The quantity of the WAP of indoor arrangement；It is exactly the current position of user to obtain the maximum starting point of probable value：SP=argmaxp
(S|SPn)。
Calculated with company as procedure described above, it is possible to calculate the probability of each possible position, so as to filter out most
Maximum probability position, and user positioned accordingly.
According to one of example, wherein, in the calculating of the probability of RSS values, only calculate signal strength signal intensity -30dBm to -
The distribution probability P of the RSS values of 90dBmrss.In view of the validity of signal strength values, we are only recorded from -30dBm to -90dBm
RSS values distribution probability.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to assert
The present invention be embodied as be confined to these explanations.For general technical staff of the technical field of the invention,
On the premise of without departing from present inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the present invention's
Protection domain.
Claims (10)
1. a kind of localization method of the start position of indoor positioning, it is characterised in that include：
The finger print data of collection intelligent terminal position, sets up finger print data starting point fingerprint base corresponding with position；
The current finger print data of intelligent terminal is obtained, and each of intelligent terminal in starting point fingerprint base is obtained according to finger print data
The location probability of position；
Maximum in the location probability one is obtained, the maximum corresponding position of the location probability is intelligent terminal
Start position.
2. method according to claim 1, it is characterised in that the finger print data includes：RSS vector sum coordinate values；Institute
The finger print data of collection intelligent terminal position is stated, the step of set up finger print data corresponding with position starting point fingerprint base
Specifically include：
Multiple datum nodes are set in positioning region；
RSS vector sum corresponding coordinate value of the collection intelligent terminal in datum node；
It is vectorial according to the RSS of each datum node, calculate the RSS averages and mean variance of each datum node, and as
The finger print data of the datum node is stored in fingerprint database.
3. method according to claim 2, it is characterised in that intelligent terminal is obtained in starting point fingerprint base according to finger print data
In the calculation procedure of location probability of each position specifically include：Wherein, RSS averages are respectively with mean variance：μ=E
(RSSi), σ=E (| RSSi- μ |), wherein, i is natural number, and u is the average of RSS, and σ is the mean variance of RSS, and each is with reference to knot
The RSS values size of point is Gauss normal distribution in reference point locations, i.e.,
The probability of the RSS values is：
Wherein PrssFor the probability of the RSS values of datum node, x is current reference
The x-axis coordinate value of node, u is the average of RSS, and σ is the mean variance of RSS.
4. method according to claim 3, it is characterised in that the calculation procedure is further included：According toProbability is calculated, wherein m represents the number of the RSS vectors of collection, Sj={ RSSj1,
RSSj2..., RSSjnA RSS vector is represented, n is the quantity of the WAP of indoor arrangement；Obtain probable value maximum
Starting point is exactly the current position of user：
SP=argmaxp (S | SPn)。
5. method according to claim 3, it is characterised in that wherein, in the calculating of the probability of RSS values, only calculate signal
Distribution probability P of the intensity in the RSS values of -30dBm to -90dBmrss。
6. a kind of alignment system of the start position of indoor positioning, it is characterised in that include：
Data acquisition module, for gathering the finger print data of intelligent terminal position, sets up finger print data and position pair
The starting point fingerprint base answered；
Acquisition module, the finger print data current for obtaining intelligent terminal, and intelligent terminal is obtained in starting point according to finger print data
The location probability of each position in fingerprint base；
Processing module, for obtaining the location probability in maximum one, the maximum corresponding institute of the location probability is in place
It is set to the start position of intelligent terminal.
7. system according to claim 6, it is characterised in that the finger print data includes：RSS vector sum coordinate values；Institute
State data acquisition module specifically for：
Multiple datum nodes are set in positioning region；
RSS vector sum corresponding coordinate value of the collection intelligent terminal in datum node；
It is vectorial according to the RSS of each datum node, calculate the RSS averages and mean variance of each datum node, and as
The finger print data of the datum node is stored in fingerprint database.
8. system according to claim 7, it is characterised in that the acquisition module specifically for：Wherein, RSS averages with
Mean variance is respectively：μ=E (RSSi), σ=E (| RSSi- μ |), wherein, i is natural number, and u is the average of RSS, and σ is RSS's
Mean variance, the RSS values size of each datum node is Gauss normal distribution in reference point locations, i.e.,
The probability of the RSS values is：
Wherein PrssFor the probability of the RSS values of datum node, x is current reference
The x-axis coordinate value of node, u is the average of RSS, and σ is the mean variance of RSS.
9. system according to claim 8, it is characterised in that the acquisition module specifically for：According toProbability is calculated, wherein m represents the number of the RSS vectors of collection, Sj={ RSSj1,
RSSj2..., RSSjnA RSS vector is represented, n is the quantity of the WAP of indoor arrangement；Obtain probable value maximum
Starting point is exactly the current position of user：SP=argmaxp (S | SPn)。
10. system according to claim 8, it is characterised in that wherein, in the calculating of the probability of RSS values, only calculate signal
Distribution probability P of the intensity in the RSS values of -30dBm to -90dBmrss。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611048962.XA CN106658704A (en) | 2016-11-23 | 2016-11-23 | Positioning method and system of starting point of indoor positioning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611048962.XA CN106658704A (en) | 2016-11-23 | 2016-11-23 | Positioning method and system of starting point of indoor positioning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106658704A true CN106658704A (en) | 2017-05-10 |
Family
ID=58812435
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611048962.XA Pending CN106658704A (en) | 2016-11-23 | 2016-11-23 | Positioning method and system of starting point of indoor positioning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106658704A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107219500A (en) * | 2017-06-01 | 2017-09-29 | 成都希盟泰克科技发展有限公司 | Indoor rapid integrated localization method based on WIFI location fingerprint data |
| CN108289283A (en) * | 2018-01-02 | 2018-07-17 | 重庆邮电大学 | User trajectory localization method based on sequences match under indoor DAS system |
| CN108882169A (en) * | 2018-04-10 | 2018-11-23 | 北京三快在线科技有限公司 | The acquisition methods and device and robot of a kind of WiFi location fingerprint data |
| CN109587627A (en) * | 2018-12-12 | 2019-04-05 | 嘉兴学院 | The indoor positioning algorithms of terminal heterogeneity are improved based on RSSI |
| CN109581285A (en) * | 2018-12-13 | 2019-04-05 | 成都普连众通科技有限公司 | A kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data |
| CN109767141A (en) * | 2019-03-07 | 2019-05-17 | 辽宁北斗卫星导航平台有限公司 | A monitoring method, device, medium and equipment for railway patrol inspection |
| CN109782324A (en) * | 2019-03-07 | 2019-05-21 | 辽宁北斗卫星位置信息服务有限公司 | A kind of patrolling railway localization method |
| CN110361693A (en) * | 2019-07-15 | 2019-10-22 | 黑龙江大学 | A kind of indoor orientation method based on probability fingerprint |
| CN110940951A (en) * | 2018-09-25 | 2020-03-31 | 北京四维图新科技股份有限公司 | Positioning method and device |
| CN111726860A (en) * | 2020-06-09 | 2020-09-29 | 北京无限向溯科技有限公司 | Positioning method, device, equipment and storage medium based on POI space distance |
| CN112533144A (en) * | 2019-09-19 | 2021-03-19 | 中国移动通信集团辽宁有限公司 | Indoor positioning method and device, computing equipment and computer storage medium |
| CN112822637A (en) * | 2021-01-06 | 2021-05-18 | 腾讯科技（深圳）有限公司 | Navigation starting road section determining method and device, electronic equipment and storage medium |
| CN114646917A (en) * | 2022-03-07 | 2022-06-21 | 北京华信傲天网络技术有限公司 | Indoor positioning method based on RSSI fingerprint |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040152470A1 (en) * | 2003-02-04 | 2004-08-05 | Spain David Stevenson | Location estimation of wireless terminals though pattern matching of signal-strength differentials |
| CN103402256A (en) * | 2013-07-11 | 2013-11-20 | 武汉大学 | Indoor positioning method based on WiFi (Wireless Fidelity) fingerprints |
| CN103916954A (en) * | 2013-01-07 | 2014-07-09 | 华为技术有限公司 | Probability locating method and locating device based on WLAN |
| CN105137390A (en) * | 2015-09-14 | 2015-12-09 | 上海工程技术大学 | Indoor positioning method based on AP with adjustable transmitted power |
| CN105208651A (en) * | 2015-08-17 | 2015-12-30 | 上海交通大学 | Wi-Fi position fingerprint non-monitoring training method based on map structure |
| CN105392196A (en) * | 2015-12-04 | 2016-03-09 | 京信通信技术(广州)有限公司 | Positioning method and device |
| CN105516931A (en) * | 2016-02-29 | 2016-04-20 | 重庆邮电大学 | Indoor differential positioning method on basis of double-frequency WLAN (wireless local area network) access points |
-
2016
- 2016-11-23 CN CN201611048962.XA patent/CN106658704A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040152470A1 (en) * | 2003-02-04 | 2004-08-05 | Spain David Stevenson | Location estimation of wireless terminals though pattern matching of signal-strength differentials |
| CN103916954A (en) * | 2013-01-07 | 2014-07-09 | 华为技术有限公司 | Probability locating method and locating device based on WLAN |
| CN103402256A (en) * | 2013-07-11 | 2013-11-20 | 武汉大学 | Indoor positioning method based on WiFi (Wireless Fidelity) fingerprints |
| CN105208651A (en) * | 2015-08-17 | 2015-12-30 | 上海交通大学 | Wi-Fi position fingerprint non-monitoring training method based on map structure |
| CN105137390A (en) * | 2015-09-14 | 2015-12-09 | 上海工程技术大学 | Indoor positioning method based on AP with adjustable transmitted power |
| CN105392196A (en) * | 2015-12-04 | 2016-03-09 | 京信通信技术(广州)有限公司 | Positioning method and device |
| CN105516931A (en) * | 2016-02-29 | 2016-04-20 | 重庆邮电大学 | Indoor differential positioning method on basis of double-frequency WLAN (wireless local area network) access points |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107219500B (en) * | 2017-06-01 | 2019-12-03 | 成都希盟泰克科技发展有限公司 | The rapid integrated localization method in interior based on WIFI location fingerprint data |
| CN107219500A (en) * | 2017-06-01 | 2017-09-29 | 成都希盟泰克科技发展有限公司 | Indoor rapid integrated localization method based on WIFI location fingerprint data |
| CN108289283A (en) * | 2018-01-02 | 2018-07-17 | 重庆邮电大学 | User trajectory localization method based on sequences match under indoor DAS system |
| CN108882169A (en) * | 2018-04-10 | 2018-11-23 | 北京三快在线科技有限公司 | The acquisition methods and device and robot of a kind of WiFi location fingerprint data |
| CN110940951A (en) * | 2018-09-25 | 2020-03-31 | 北京四维图新科技股份有限公司 | Positioning method and device |
| CN109587627A (en) * | 2018-12-12 | 2019-04-05 | 嘉兴学院 | The indoor positioning algorithms of terminal heterogeneity are improved based on RSSI |
| CN109581285A (en) * | 2018-12-13 | 2019-04-05 | 成都普连众通科技有限公司 | A kind of fingerprinting localization algorithm based on the filtering of motor behavior discrete data |
| CN109782324A (en) * | 2019-03-07 | 2019-05-21 | 辽宁北斗卫星位置信息服务有限公司 | A kind of patrolling railway localization method |
| CN109767141A (en) * | 2019-03-07 | 2019-05-17 | 辽宁北斗卫星导航平台有限公司 | A monitoring method, device, medium and equipment for railway patrol inspection |
| CN110361693A (en) * | 2019-07-15 | 2019-10-22 | 黑龙江大学 | A kind of indoor orientation method based on probability fingerprint |
| CN112533144A (en) * | 2019-09-19 | 2021-03-19 | 中国移动通信集团辽宁有限公司 | Indoor positioning method and device, computing equipment and computer storage medium |
| CN111726860A (en) * | 2020-06-09 | 2020-09-29 | 北京无限向溯科技有限公司 | Positioning method, device, equipment and storage medium based on POI space distance |
| CN112822637A (en) * | 2021-01-06 | 2021-05-18 | 腾讯科技（深圳）有限公司 | Navigation starting road section determining method and device, electronic equipment and storage medium |
| CN112822637B (en) * | 2021-01-06 | 2022-03-29 | 腾讯科技（深圳）有限公司 | Navigation starting road section determining method and device, electronic equipment and storage medium |
| CN114646917A (en) * | 2022-03-07 | 2022-06-21 | 北京华信傲天网络技术有限公司 | Indoor positioning method based on RSSI fingerprint |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106658704A (en) | Positioning method and system of starting point of indoor positioning | |
| CN103068035B (en) | A kind of wireless network localization method, Apparatus and system | |
| CN102480677B (en) | A kind of determination method and apparatus of fingerprint positioning error | |
| CN103634901B (en) | Novel location fingerprint based on Density Estimator gathers extracting method | |
| CN107302793B (en) | Positioning method, server, terminal and system based on wireless signals | |
| CN104717744B (en) | A kind of indoor orientation method based on WLAN and hierarchical cluster | |
| CN109275095A (en) | A kind of indoor locating system based on bluetooth, positioning device and localization method | |
| CN106597363A (en) | Pedestrian location method in indoor WLAN environment | |
| CN102932911A (en) | Positioning method and positioning system of location fingerprints | |
| CN103200520A (en) | Mobile terminal fast and precise positioning method using Wi-Fi | |
| CN105898713A (en) | WiFi fingerprint indoor positioning method based on weighted cosine similarity | |
| CN103220777A (en) | Mobile device positioning system | |
| CN105120479B (en) | Signal strength difference correction method for Wi-Fi signals between terminals | |
| CN103826299B (en) | The indoor augmented reality implementation method perceived based on wireless signal | |
| CN107360552B (en) | Indoor positioning method for global dynamic fusion of multiple classifiers | |
| CN107302794A (en) | The method of running fix and navigation is used as by the use of WIFI signal | |
| CN109511085A (en) | A kind of UWB fingerprint positioning method based on MeanShift and weighting k nearest neighbor algorithm | |
| Chiu et al. | Implementation and analysis of hybrid wireless indoor positioning with ibeacon and wi-fi | |
| CN109068274A (en) | A kind of complex indoor environment object localization method under fine granularity fingerprint quality auxiliary | |
| CN109143161A (en) | High-precision indoor orientation method based on mixed-fingerprint Environmental Evaluation Model | |
| Junoh et al. | Enhancing indoor localization with semi-crowdsourced fingerprinting and gan-based data augmentation | |
| CN105307262B (en) | A kind of localization method and device of pseudo-base station | |
| CN105898711B (en) | A kind of localization method and device based on earth magnetism reference line | |
| CN105992146A (en) | Positioning data generation method and device | |
| CN103905993B (en) | Region misjudgment solution method based on indoor positioning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20170510 |