CN107426687A - The method for adaptive kalman filtering of positioning is merged in towards WiFi/PDR rooms - Google Patents
The method for adaptive kalman filtering of positioning is merged in towards WiFi/PDR rooms Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
-
- 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
- G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
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- 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
- G01S5/14—Determining absolute distances from a plurality of spaced points of known location
Abstract
The invention discloses a kind of towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms, user's hand-held terminal device first is in target area, receive the RSSI from each AP, under the path loss model of logarithm normal distribution, the position of user is obtained using weighted least-squares method, customer location is obtained by the appraising model in PDR algorithms simultaneously, then will repeatedly be merged with PDR location information based on the location information of propagation model using adaptive Kalman filter, obtain the optimum position of user, wherein, adaptive Kalman filter is embodied on feedback mechanism, positioning result will be merged every time, and dynamic corrections are carried out to parameters such as the path loss indexes in weighted least-squares method, finally propagation model is caused more to meet indoor environment.The present invention solves the problems, such as that single WiFi positioning precisions are low under environment indoors and PDR has accumulated error, additionally it is possible to the path loss index of real-time tracking propagation model, enhances the stability of positioning performance.
Description
Technical field
The invention belongs to indoor positioning technologies, and in particular to a kind of towards the adaptive card that positioning is merged in WiFi/PDR rooms
Kalman Filtering method.
Background technology
With the rapid development of mobile communication, location Based service LBS (Location Based Service) by
Increasing concern, and indoors place (such as market, airport and underground parking), existing outdoor positioning system, such as
Global position system GPS (Global Positioning System), due to being covered by facilities such as buildings, it is difficult to
Indoor realization is accurately positioned.At the same time, it is big due to WLAN WLAN (Wireless Local Area Network)
Scaledeployment and WLAN High-speed wireless access it is widely available, indoor user is positioned using existing WLAN infrastructure
Increasingly it is valued by people, wherein, based on received signal strength RSSI (Received Signal Strength
Indication indoor WLAN location technologies) are even more in-depth study by extensive.At the same time, smart mobile phone is also more next
More to be favored by people, they also include many advanced hardware facilities in addition to it can provide more preferable software function,
Such as WiFi module, bluetooth module and various inertial sensors, researcher directly can be developed using these hardware facilities
Indoor locating system.
Positioning basic skills based on RSSI is generally divided into location fingerprint method and propagation model method.Location fingerprint method include from
Line stage and on-line stage, in on-line stage, user utilizes freshly harvested RSSI value, the location fingerprint built with reference to off-line phase
Database and matching algorithm is searched for accordingly, realize the positioning to user, but when environment once changing, fingerprint database then should
Work as renewal, and renewal process needs to consume substantial amounts of man power and material.For propagation model method, then finger print data need not be built
Storehouse, the distance between AP (Access Point, wireless access node) and user are determined using rssi measurement value, then pass through structure
Hyperbolic function is made, determines the position of user.But subject matter existing for propagation model method is to need an accurate channel mould
Type, due to the dynamic of environment, the accurate parameter in Real-time Channel model is hardly resulted in, is also difficult to obtain between AP and user
Accurate distance so that positioning result may have severe deviations.In addition, the PDR based on mobile terminal inertial sensor
(Pedestrian Dead Reckoning, pedestrian's dead reckoning) algorithm has positioning precision height in the short time, but position error
When gradually increasing with time integral, and utilizing WiFi positioning, stand-alone position error is big but does not have the accumulation of error, above-mentioned in order to solve
The path loss index of problem and real-time tracking propagation model, the present invention propose a kind of towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering.
The content of the invention
It is an object of the invention to provide a kind of towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms,
It can effectively solve the problems, such as that WiFi Point-positioning Precisions are low under environment indoors and PDR has accumulated error, meanwhile, this method
The path loss index of energy real-time tracking propagation model, strengthen the stability of positioning performance.
It is of the present invention towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms, including following step
Suddenly：
Step 1: in target area, the initial position X ' of user is given0|0；
Step 2: user receives i-th of AP signal intensityI=1 ..., m, m are AP number；
Step 3: the path loss model according to logarithm normal distributionWherein,
A is represented in reference distance d0The signal intensity that place receives；η represents path loss index；N0Ambient noise is represented, is obeyed equal
It is worth for 0, variances sigma2Gaussian Profile, calculate the distance between user and each AP, wherein, diRepresent user and APiBetween away from
From APiRepresent i-th of AP；
Step 4: known AP1,…,APmPosition coordinates be respectively (x1,y1),…,(xm,ym), and set the position of user
Coordinate is (x, y), the distance between the user obtained according to step 3 and each AP, constructs Hyperbolic Equation；
Step 5: using weighted least-squares method, weight is assigned to the distance between user and each AP, calculates and is based on
WiFi estimated location
Step 6: known users initial position X '0|0, the relative positioning to user is realized using PDR algorithms, and then calculate
Go out user in tkPosition (the x at momentk,yk)；
Step 7: in the forecast period of Kalman filtering, predicted state X ' is obtainedk|k-1With to should predicted state association side
Poor Pkk-1；
Step 8: in the more new stage of Kalman filtering, kalman gain K is obtainedkWith the estimated state at current time
X′k|k；
Step 9: the estimated result X ' using Kalman filterk|kAs input, alignment path loss index；
Can convergent iteration threshold Step 10: settingWherein,WithIt is X-axis and Y-axis side respectively
To variance, i.e. covariance matrix PkkElement on diagonal, α is adjustability coefficients, and is calculated
Wherein,The estimated location after the renewal of kth moment iteration j is represented,Represent at the kth moment the
Estimated location after j-1 iteration renewal；
Step 11: judgeWhether threshold value T is less than；It is, then into step 12；It is no, then
Into step 9；
Step 12: renewal rssi measurement error covariance σR；
Step 13: renewal kalman gain Kk, current time estimated stateWith covariance matrix Pkk；
Step 14: obtain the optimum position of userAnd as the initial position of subsequent time.
The step 5 comprises the following steps：
5a, according to step 4, the Least Square Method based on WiFi isWherein,Between user and i-th of AP
Estimated distance, think that the distance between user and each AP have identical weight to positioning result herein, but channel model
Belong to nonlinear model, therefore the path loss model based on logarithm normal distribution, user is identical in different transmission ranges
The rssi measurement error of Gaussian Profile can cause different range measurement errors, i.e. transmission range is bigger, then range measurement error
It is bigger；So taking weighted least-squares method, its formula isWherein, S isCovariance
Matrix, less transmission range is assigned to larger weight so that the distance of estimation has higher accuracy；
5b, assume that the estimated distance between user and each AP is independent, wherein,Represent user and APiBetween estimate
Distance is counted, then covariance matrix S is：
Wherein, Var represents variance computing, that is, has
5c, the path loss model based on step 3, estimated distanceFor：
Wherein,Meet logarithm normal distribution, make μd=ln diAndThen stochastic variableK rank origins
Square is
5d, according to step 5c, can obtain：
5e, according to step 5d, can obtainBy
In σdVariance for constant and each estimated distance contains σd, therefore about fall invariant from covariance matrix SIt can't influence to be based on weighted least-squares method estimated location
I.e.It is equivalent to
5f, based on step 5e, can obtain
5g, covariance matrix S is expressed as：
5h therefore the estimated location based on WiFi
The step 9 comprises the following steps：
9a, according to step 3, can obtain：
9b, using step 8 current time estimated state X 'k|k, calculateWherein,For
I-th of AP position coordinates, | | | |2Represent euclideam norm；
9c, with reference to step 9a and step 9b, obtain：
Wherein,For currently on the path loss index after i-th of AP renewal；
9d, basisRepeat step four, step 5 and step 8, obtain the estimated state at new current time
The step 12 comprises the following steps：
12a, calculatingWherein, ToldRepresent previous moment on all AP path loss index and, η ' is
I-th of AP of previous moment path loss index；
12b, calculatingWherein, TnewRepresent the path loss index after current time all AP renewals and；
12c, whenDuring renewal, while update rssi measurement error covariance σR；
Wherein：The rssi measurement error covariance after renewal is represented,Represent the rssi measurement error association before renewal
Variance.
The present invention has advantages below：User's hand-held terminal device receives the RSSI from each AP in target area,
Under the path loss model of logarithm normal distribution, the position of user is obtained using weighted least-squares method, while calculate by PDR
Appraising model in method obtains customer location, then using adaptive Kalman filter by the location information based on propagation model with
PDR location information is repeatedly merged, and obtains the optimum position of user, wherein, adaptive Kalman filter is embodied in feedback
In mechanism, positioning result will be merged every time Mobile state is entered to parameters such as the path loss indexes in weighted least-squares method and repaiied
Just, finally propagation model is caused more to meet indoor environment.Compared to traditional Kalman's fusion and positioning method, the present invention can be real-time
The path loss index of propagation model is tracked, enhances the stability of positioning performance.The present invention can apply to radio communication
Network environment, solve the problems, such as that single WiFi positioning precisions are low under environment indoors and PDR has accumulated error.
Brief description of the drawings
Fig. 1 be the present invention in step 1 to step 11 flow chart；
Fig. 2 be the present invention in step 11 to step 14 flow chart；
Fig. 3 is simulated environment schematic diagram；
Fig. 4 is RSSI collecting sample；
Fig. 5 is the position error cumulative distribution function comparison diagram of the distinct methods under the first paths；
Fig. 6 is the track comparison diagram of the distinct methods under the first paths；
Fig. 7 is the average localization error comparison diagram of the distinct methods under the first paths；
Fig. 8 is the position error cumulative distribution function comparison diagram of the RSSI before and after renewal η under the first paths；
Fig. 9 is the position error cumulative distribution function comparison diagram under the second paths；
Figure 10 is the track comparison diagram of the distinct methods under the second paths；
Figure 11 is the average localization error comparison diagram of the distinct methods under the second paths；
Figure 12 is the position error cumulative distribution function comparison diagram of the RSSI before and after renewal η under the second paths；
Figure 13 is the position error cumulative distribution function comparison diagram under third path；
Figure 14 is the track comparison diagram of the distinct methods under third path；
Figure 15 is the average localization error comparison diagram of the distinct methods under third path；
Figure 16 is the position error cumulative distribution function comparison diagram of the RSSI before and after renewal η under third path；
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.
As shown in Figure 1 to Figure 2 towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms, including with
Lower step：
Step 1: in target area, the initial position X ' of user is given0|0。
Step 2: user receives i-th of AP signal intensity(i=1 ..., m, m are AP number).
Step 3: the path loss model according to logarithm normal distributionWherein,
A is represented in reference distance d0The signal intensity that place receives；η represents path loss index；N0Ambient noise is represented, is obeyed equal
It is worth for 0, variances sigma2Gaussian Profile, calculate the distance between user and each AP, wherein, diRepresent user and i-th of AP (i.e.
APiThe distance between).
Step 4: known AP1,…,APmPosition coordinates be respectively (x1,y1),…,(xm,ym), and set the position of user
Coordinate is (x, y), the distance between the user obtained according to step 3 and each AP, constructs Hyperbolic Equation；Specifically include with
Lower step：
4a, Hyperbolic Equation are：
4b, step 4a Hyperbolic Equation is deployed and can be obtained after eliminating quadratic term：
4c, due to measurement error be present, so we can only obtain estimated distance valueFormula is expressed in the matrix form
For：
Wherein,
Step 5: using weighted least-squares method, weight is assigned to the distance between user and each AP, calculates and is based on
WiFi estimated locationSpecifically include following steps：
5a, according to step 4, the Least Square Method based on WiFi isWherein,Between user and i-th of AP
Estimated distance, think that the distance between user and each AP have identical weight to positioning result herein, but channel model
Belong to nonlinear model, therefore the path loss model based on logarithm normal distribution, user is identical in different transmission ranges
The rssi measurement error of Gaussian Profile can cause different range measurement errors, i.e. transmission range is bigger, then range measurement error
It is bigger.So taking weighted least-squares method, its formula isWherein, S isCovariance
Matrix, less transmission range is assigned to larger weight so that the distance of estimation has higher accuracy.
5b, assume that the estimated distance between user and each AP is independent, wherein,Represent user and APiBetween estimate
Distance is counted, then covariance matrix S is：
Wherein, Var represents variance computing, that is, has
5c, the path loss model based on step 3, estimated distanceFor：
Wherein,Meet logarithm normal distribution, make μd=lndiAndThen stochastic variableK rank moment of the origns
For
5d, according to step 5c, can obtain：
5e, according to step 5d, can obtain
Due to σdVariance for constant and each estimated distance contains σd, therefore about fall invariant from covariance matrix SIt can't influence to be based on weighted least-squares method estimated location
I.e.It is equivalent to
5f, based on step 5e, can obtain
5g, covariance matrix S is expressed as：
5h therefore the estimated location based on WiFi
Step 6: known users initial position X '0|0, the relative positioning to user is realized using PDR algorithms, and then calculate
Go out user in tkPosition (the x at momentk,yk)；Specifically include following steps：
6a, user are in t1Position (the x at moment1,y1) be：
6b, user are in t2Position (the x at moment2,y2) be：
6c, go down in prediction on such basis, then user is in tkPosition (the x at momentk,yk) be：
Wherein, (xk-1,yk-1) it is tk-1The customer location at moment, SLiIt is walking step-length or ti-1To tiThe displacement of time interval
Amount, θiIt is the direction of motion vector.Therefore the estimated location X μ of the user at current time can be speculated according to last momentk|k-1。
Step 7: in the forecast period of Kalman filtering, predicted state X ' is obtainedk|k-1With to should predicted state association side
Poor Pk|k-1；Specifically include following steps：
7a, calculate predicted state X 'k|k-1, its calculation formula is：
Wherein：Parameter k represents the kth moment,Velocity is represented, Δ T represents the update cycle of Kalman filter.
7b, calculate to should predicted state covariance Pk|k-1, its calculation formula is：
Pk|k-1=Pk-1|k-1+Qk；
Wherein Pk-1|k-1It is estimated state Xk-1|k-1Covariance matrix, QkIt is the covariance matrix of active procedure noise.
7c、QkCalculation formula be：
Wherein：I represents unit matrix, σaAnd σvThe standard deviation of acceleration transducer and velocity sensor is represented respectively.
Step 8: in the more new stage of Kalman filtering, kalman gain K is obtainedkWith the estimated state at current time
X′k|k；Specifically include following steps：
8a, calculate kalman gain Kk：
Kk=Pk|k-1CT(CPk|k-1CT+Rk)-1；
Wherein, C is observing matrix,RkIt is the covariance matrix of observation noise.
8b、RkIt is embodied as：
Wherein, Qk/INSWith QkIt is identical, Rk/RSSIIt is the covariance matrix of the positioning result based on WiFi, initial value R0/RSSIExpression
Formula is：
Wherein,It is the standard deviation of the positioning result based on WiFi, the value can be determined by experiment.
8c, the estimated state X ' at current timek|kIts calculation formula is：
X′k|k=X 'k|k-1+Kk(Zk-CX′k|k-1)
Wherein, ZkIt is the observed result from WiFi and PDR,
Step 9: the estimated result X ' using Kalman filterk|kAs input, alignment path loss index；Specific bag
Include following steps：
9a, according to step 3, can obtain：
9b, using step 8 current time estimated state X 'k|k, calculateWherein,For
I (i=1 ..., m) individual AP position coordinates, | | | |2Represent euclideam norm.
9c, with reference to step 9a and step 9b, obtain：
Wherein,For currently on the path loss index after i-th of AP renewal.
9d, basisRepeat step four, step 5 and step 8, obtain the estimated state at new current time
Can convergent iteration threshold Step 10: settingWherein,WithIt is the side of X-axis and Y direction respectively
Difference, i.e. covariance matrix PkkElement on diagonal, Pk|k=(I-KkC)Pk|k-1, α is adjustability coefficients, and is calculatedWherein,The estimated location after the renewal of kth moment iteration j is represented,Represent the estimated location after -1 iteration renewal of kth moment jth.
Step 11: judgeWhether threshold value T is less than；It is, then into step 12；It is no, then
Into step 9.
Step 12: renewal rssi measurement error covariance σR；Specifically include following steps：
12a, calculatingWherein, ToldRepresent previous moment on all AP path loss index and, η ' is
I-th of AP of previous moment path loss index；
12b, calculatingWherein, TnewRepresent the path loss index after current time all AP renewals and；
12c, whenDuring renewal, while update rssi measurement error covariance σR；
Wherein：The rssi measurement error covariance after renewal is represented,Represent the rssi measurement error association before renewal
Variance.Step 13: renewal kalman gain Kk, current time estimated stateWith covariance matrix Pkk；Specifically include
Following steps：
13a, renewal kalman gain Kk, i.e. repeat step 8a.
13b, the estimated state for updating current timeThat is repeat step 8c.
13c, renewal covariance matrix Pk|k, its calculation formula is：
Pk|k=(I-KkC)Pk|k-1。
Step 14: obtain the optimum position of userAnd as the initial position of subsequent time.
As shown in figure 3, the area of simulated environment is 57m × 25m, 6 AP, model DLINK DAP are distributed altogether
2310, its coordinate be respectively (0m, 3m), (11.6m, 7.44m), (24.8m, 11.04m), (15.6m, 0m), (56.5m, 0m),
(51m, 16.8m), in this experiment, in order to verify the validity of the inventive method, path 1. straight line moving is devised, path is 2.
Turn back walking, 3. broken line is walked in path.
RSSI collecting sample is illustrated in figure 4, MAC Address and timestamp comprising signal strength values, AP.
As shown in figure 5, path is given 1. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, the error accumulation probability distribution contrast of Kalman's fusion method.It can be seen that in reality
Test in result, there is higher positioning accurate using towards the method for adaptive kalman filtering for merging positioning in WiFi/PDR rooms
Degree.
As shown in fig. 6, path is given 1. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, the run trace contrast of Kalman's fusion method.It can be seen that in run trace
In, using towards the track that the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms is walked closer to true rail
Mark.
As shown in fig. 7, path is given 1. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, the average localization error contrast of Kalman's fusion method.It can be seen that tied in experiment
It is minimum using the average localization error towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms in fruit.
As shown in figure 8, path is given 1. under experimental situation, the position error cumulative distribution of the RSSI before and after renewal η
Function comparison diagram.It can be seen that in experimental result, the positioning precision of the RSSI after renewal η significantly improves.
As shown in figure 9, path is given 2. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, the error accumulation probability distribution contrast of Kalman's fusion method.It can be seen that in reality
Test in result, there is higher positioning accurate using towards the method for adaptive kalman filtering for merging positioning in WiFi/PDR rooms
Degree.
As shown in Figure 10, path is given 2. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, Kalman's fusion method run trace contrast.It can be seen that in run trace
In, using towards the track that the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms is walked closer to true rail
Mark.
As shown in figure 11, path is given 2. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, Kalman's fusion method average localization error contrast.It can be seen that testing
As a result it is minimum using the average localization error towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms in.
As shown in figure 12, path is given 2. under experimental situation, the position error cumulative distribution of the RSSI before and after renewal η
Function comparison diagram.It can be seen that in experimental result, the positioning precision of the RSSI after renewal η significantly improves.
As shown in figure 13, path is given 3. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, Kalman's fusion method error accumulation probability distribution contrast.It can be seen that
In experimental result, there is higher positioning accurate using towards the method for adaptive kalman filtering for merging positioning in WiFi/PDR rooms
Degree.
As shown in figure 14, path is given 3. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, Kalman's fusion method run trace contrast.It can be seen that in run trace
In, using towards the track that the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms is walked closer to true rail
Mark.
As shown in figure 15, path is given 3. under experimental situation, it is of the invention towards fusion positioning in WiFi/PDR rooms
Method for adaptive kalman filtering and PDR, RSSI, Kalman's fusion method average localization error contrast.It can be seen that testing
As a result it is minimum using the average localization error towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms in.
As shown in figure 16, path is given 3. under experimental situation, the position error cumulative distribution of the RSSI before and after renewal η
Function comparison diagram.It can be seen that in experimental result, the positioning precision of the RSSI after renewal η significantly improves.
Claims (4)
1. the method for adaptive kalman filtering of positioning is merged in towards WiFi/PDR rooms, it is characterised in that comprise the following steps：
Step 1: in target area, the initial position X ' of user is given0|0；
Step 2: user receives i-th of AP signal intensityI=1 ..., m, m are AP number；
Step 3: the path loss model according to logarithm normal distributionWherein, A is represented
In reference distance d0The signal intensity that place receives；η represents path loss index；N0Ambient noise is represented, it is 0 to obey average,
Variances sigma2Gaussian Profile, calculate the distance between user and each AP, wherein, diRepresent user and APiThe distance between, APi
Represent i-th of AP；
Step 4: known AP1,…,APmPosition coordinates be respectively (x1,y1),…,(xm,ym), and set the position coordinates of user
For (x, y), the distance between the user obtained according to step 3 and each AP, Hyperbolic Equation is constructed；
Step 5: using weighted least-squares method, weight is assigned to the distance between user and each AP, is calculated based on WiFi
Estimated location
Step 6: known users initial position X '0|0, the relative positioning to user is realized using PDR algorithms, and then extrapolate use
Family is in tkPosition (the x at momentk,yk)；
Step 7: in the forecast period of Kalman filtering, predicted state X ' is obtainedk|k-1With to should predicted state covariance
Pk|k-1；
Step 8: in the more new stage of Kalman filtering, kalman gain K is obtainedkWith the estimated state X ' at current timek|k；
Step 9: the estimated result X ' using Kalman filterk|kAs input, alignment path loss index；
Can convergent iteration threshold Step 10: settingWherein,WithIt is X-axis and Y direction respectively
Variance, i.e. covariance matrix Pk|kElement on diagonal, α is adjustability coefficients, and is calculatedIts
In,The estimated location after the renewal of kth moment iteration j is represented,Represent in kth moment jth -1
Estimated location after secondary iteration renewal；
Step 11: judgeWhether threshold value T is less than；It is, then into step 12；It is no, then enter
Step 9；
Step 12: renewal rssi measurement error covariance σR；
Step 13: renewal kalman gain Kk, current time estimated stateWith covariance matrix Pk|k；
Step 14: obtain the optimum position of userAnd as the initial position of subsequent time.
2. according to claim 1 towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms, it is special
Sign is：The step 5 comprises the following steps：
5a, according to step 4, the Least Square Method based on WiFi isWherein, Between user and i-th of AP
Estimated distance, think that the distance between user and each AP have identical weight to positioning result herein, but channel model
Belong to nonlinear model, therefore the path loss model based on logarithm normal distribution, user is identical in different transmission ranges
The rssi measurement error of Gaussian Profile can cause different range measurement errors, i.e. transmission range is bigger, then range measurement error
It is bigger；So taking weighted least-squares method, its formula isWherein, S isCovariance
Matrix, less transmission range is assigned to larger weight so that the distance of estimation has higher accuracy；
5b, assume that the estimated distance between user and each AP is independent, wherein,Represent user and APiBetween estimation away from
From then covariance matrix S is：
Wherein, Var represents variance computing, that is, has
5c, the path loss model based on step 3, estimated distanceFor：
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<mi>&sigma;</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mn>10</mn>
<mi>&eta;</mi>
</mrow>
</mfrac>
</msup>
<mo>=</mo>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mo>(</mo>
<mrow>
<mi>ln</mi>
<mi> </mi>
<msub>
<mi>d</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<mfrac>
<mrow>
<mi>&sigma;</mi>
<mi>l</mi>
<mi>n</mi>
<mn>10</mn>
</mrow>
<mrow>
<mn>10</mn>
<mi>&eta;</mi>
</mrow>
</mfrac>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein,Meet logarithm normal distribution, make μd=lndiAndThen stochastic variableK rank moment of the origns be
5d, according to step 5c, can obtain：
<mrow>
<mi>E</mi>
<mo>&lsqb;</mo>
<msubsup>
<mover>
<mi>d</mi>
<mo>~</mo>
</mover>
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<mn>4</mn>
</msubsup>
<mo>&rsqb;</mo>
<mo>=</mo>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mn>4</mn>
<msub>
<mi>&mu;</mi>
<mi>d</mi>
</msub>
<mo>+</mo>
<mn>8</mn>
<msubsup>
<mi>&sigma;</mi>
<mi>d</mi>
<mn>2</mn>
</msubsup>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
<mrow>
<mi>E</mi>
<mo>&lsqb;</mo>
<msubsup>
<mover>
<mi>d</mi>
<mo>~</mo>
</mover>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mo>&rsqb;</mo>
<mo>=</mo>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mn>2</mn>
<msub>
<mi>&mu;</mi>
<mi>d</mi>
</msub>
<mo>+</mo>
<mn>2</mn>
<msubsup>
<mi>&sigma;</mi>
<mi>d</mi>
<mn>2</mn>
</msubsup>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
5e, according to step 5d, can obtainBy
In σdVariance for constant and each estimated distance contains σd, therefore about fall invariant from covariance matrix SIt can't influence to be based on weighted least-squares method estimated locationI.e.It is equivalent to
5f, based on step 5e, can obtain
5g, covariance matrix S is expressed as：
5h therefore the estimated location based on WiFi
3. it is according to claim 1 or 2 towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms, its
It is characterised by：The step 9 comprises the following steps：
9a, according to step 3, can obtain：
<mrow>
<mi>&eta;</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mo>(</mo>
<mi>A</mi>
<mo>-</mo>
<msub>
<mi>P</mi>
<mrow>
<msub>
<mi>RX</mi>
<mi>i</mi>
</msub>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>N</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<msub>
<mn>101</mn>
<mn>0</mn>
</msub>
<mi>g</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>d</mi>
<mi>i</mi>
</msub>
<mo>/</mo>
<msub>
<mi>d</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
9b, using step 8 current time estimated state X 'k|k, calculateWherein,For i-th
AP position coordinates, | | | |2Represent euclideam norm；
9c, with reference to step 9a and step 9b, obtain：
<mrow>
<msubsup>
<mi>&eta;</mi>
<mi>i</mi>
<mrow>
<mo>&prime;</mo>
<mi>n</mi>
<mi>e</mi>
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</mrow>
<mrow>
<mn>10</mn>
<mi>l</mi>
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<mi>g</mi>
<mrow>
<mo>(</mo>
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<mo>|</mo>
<msubsup>
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<mrow>
<mi>k</mi>
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<mi>k</mi>
</mrow>
<mo>&prime;</mo>
</msubsup>
<mo>-</mo>
<msub>
<mi>X</mi>
<mrow>
<msub>
<mi>AP</mi>
<mi>i</mi>
</msub>
</mrow>
</msub>
<mo>|</mo>
<msub>
<mo>|</mo>
<mn>2</mn>
</msub>
<mo>/</mo>
<msub>
<mi>d</mi>
<mn>0</mn>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
Wherein,For currently on the path loss index after i-th of AP renewal；
9d, basisRepeat step four, step 5 and step 8, obtain the estimated state at new current time
4. according to claim 3 towards the method for adaptive kalman filtering that positioning is merged in WiFi/PDR rooms, it is special
Sign is：The step 12 comprises the following steps：
12a, calculatingWherein, ToldRepresent previous moment on all AP path loss index and, η ' is previous
I-th of AP of moment path loss index；
12b, calculatingWherein, TnewRepresent the path loss index after current time all AP renewals and；
12c, whenDuring renewal, while update rssi measurement error covariance σR；
<mrow>
<msubsup>
<mi>&sigma;</mi>
<mi>R</mi>
<mrow>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msubsup>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msubsup>
<mi>&eta;</mi>
<mi>i</mi>
<mo>&prime;</mo>
</msubsup>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msubsup>
<mi>&eta;</mi>
<mi>i</mi>
<mrow>
<mo>&prime;</mo>
<mi>n</mi>
<mi>e</mi>
<mi>w</mi>
</mrow>
</msubsup>
</mrow>
</mfrac>
<msubsup>
<mi>&sigma;</mi>
<mi>R</mi>
<mrow>
<mi>o</mi>
<mi>l</mi>
<mi>d</mi>
</mrow>
</msubsup>
<mo>;</mo>
</mrow>
Wherein：The rssi measurement error covariance after renewal is represented,Represent the rssi measurement error association side before renewal
Difference.
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| CN109005503A (en) * | 2018-08-13 | 2018-12-14 | 电子科技大学 | A kind of fusion and positioning method based on WiFi and PDR |
| CN109115228A (en) * | 2018-10-23 | 2019-01-01 | 中国科学院声学研究所 | A kind of object localization method based on weighted least-squares volume Kalman filtering |
| CN109211229A (en) * | 2018-08-26 | 2019-01-15 | 桂林电子科技大学 | A kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature |
| CN109413578A (en) * | 2018-11-02 | 2019-03-01 | 桂林电子科技大学 | A kind of indoor orientation method merged based on WIFI with PDR |
| CN109506653A (en) * | 2018-11-12 | 2019-03-22 | 上海理工大学 | Based on the indoor positioning pedestrian's method for improving particle filter under NLOS environment |
| CN110231592A (en) * | 2019-04-11 | 2019-09-13 | 深圳市城市交通规划设计研究中心有限公司 | Indoor orientation method, device, computer readable storage medium and terminal device |
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| CN110530359A (en) * | 2019-09-10 | 2019-12-03 | 中国计量大学 | It is a kind of to be corrected based on bluetooth and map match seeks an indoor orientation method |
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| CN111970633A (en) * | 2020-08-24 | 2020-11-20 | 桂林电子科技大学 | Indoor positioning method based on WiFi, Bluetooth and pedestrian dead reckoning fusion |
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| CN112187301A (en) * | 2020-09-14 | 2021-01-05 | 中标慧安信息技术股份有限公司 | Millimeter wave signal recovery method |
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| CN114245459A (en) * | 2022-02-23 | 2022-03-25 | 深圳市海豚科技创新有限公司 | Fusion positioning method and device based on PDR model, computer equipment and medium |
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| CN114710744A (en) * | 2022-04-12 | 2022-07-05 | 电子科技大学 | Indoor positioning method integrating WiFi ranging and PDR calculation deeply |
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| CN109005503B (en) * | 2018-08-13 | 2020-05-19 | 电子科技大学 | Fusion positioning method based on WiFi and PDR |
| CN109211229A (en) * | 2018-08-26 | 2019-01-15 | 桂林电子科技大学 | A kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature |
| CN109115228A (en) * | 2018-10-23 | 2019-01-01 | 中国科学院声学研究所 | A kind of object localization method based on weighted least-squares volume Kalman filtering |
| CN109115228B (en) * | 2018-10-23 | 2020-08-18 | 中国科学院声学研究所 | Target positioning method based on weighted least square volume Kalman filtering |
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| CN110231592A (en) * | 2019-04-11 | 2019-09-13 | 深圳市城市交通规划设计研究中心有限公司 | Indoor orientation method, device, computer readable storage medium and terminal device |
| CN110366109A (en) * | 2019-08-01 | 2019-10-22 | 厦门大学 | A kind of localization method and system for indoor objects |
| CN110530359A (en) * | 2019-09-10 | 2019-12-03 | 中国计量大学 | It is a kind of to be corrected based on bluetooth and map match seeks an indoor orientation method |
| CN111010664A (en) * | 2019-12-06 | 2020-04-14 | 西安科技大学 | Indoor positioning method and device |
| CN111010664B (en) * | 2019-12-06 | 2021-10-26 | 西安科技大学 | Indoor positioning method and device |
| CN111836191A (en) * | 2020-07-22 | 2020-10-27 | 珠海格力电器股份有限公司 | Positioning method, positioning device, storage medium and electronic equipment |
| CN111836191B (en) * | 2020-07-22 | 2021-08-20 | 珠海格力电器股份有限公司 | Positioning method, positioning device, storage medium and electronic equipment |
| CN111970633A (en) * | 2020-08-24 | 2020-11-20 | 桂林电子科技大学 | Indoor positioning method based on WiFi, Bluetooth and pedestrian dead reckoning fusion |
| CN112073902A (en) * | 2020-08-25 | 2020-12-11 | 中国电子科技集团公司第五十四研究所 | Multi-mode indoor positioning method |
| CN112180323A (en) * | 2020-09-08 | 2021-01-05 | 重庆邮电大学 | Wi-Fi-based TOA and AOA indoor combined positioning algorithm research |
| CN112004203A (en) * | 2020-09-14 | 2020-11-27 | 西安科技大学 | Indoor positioning method and device based on position prediction and error compensation |
| CN112187301A (en) * | 2020-09-14 | 2021-01-05 | 中标慧安信息技术股份有限公司 | Millimeter wave signal recovery method |
| CN112187301B (en) * | 2020-09-14 | 2022-02-22 | 中标慧安信息技术股份有限公司 | Millimeter wave signal recovery method |
| CN113566820A (en) * | 2021-06-17 | 2021-10-29 | 电子科技大学 | Fusion pedestrian positioning method based on position fingerprint and PDR algorithm |
| CN114594421A (en) * | 2022-02-15 | 2022-06-07 | 湖北大学 | Moving target point position calculation method based on least square method and Kalman filter |
| CN114594421B (en) * | 2022-02-15 | 2022-11-18 | 湖北大学 | Moving target point position calculation method based on least square method and Kalman filter |
| CN114245459A (en) * | 2022-02-23 | 2022-03-25 | 深圳市海豚科技创新有限公司 | Fusion positioning method and device based on PDR model, computer equipment and medium |
| CN114245459B (en) * | 2022-02-23 | 2022-06-24 | 深圳市海豚科技创新有限公司 | Fusion positioning method and device based on PDR model, computer equipment and medium |
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| CN114710744B (en) * | 2022-04-12 | 2023-03-21 | 电子科技大学 | Indoor positioning method integrating WiFi ranging and PDR calculation in depth |
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