CN110187306A - A kind of TDOA-PDR-MAP fusion and positioning method applied to the complicated interior space - Google Patents
A kind of TDOA-PDR-MAP fusion and positioning method applied to the complicated interior space Download PDFInfo
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- CN110187306A CN110187306A CN201910303807.5A CN201910303807A CN110187306A CN 110187306 A CN110187306 A CN 110187306A CN 201910303807 A CN201910303807 A CN 201910303807A CN 110187306 A CN110187306 A CN 110187306A
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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
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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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
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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
- G01S5/0257—Hybrid positioning
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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
- G01S5/12—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 by co-ordinating position lines of different shape, e.g. hyperbolic, circular, elliptical or radial
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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
- H04W4/029—Location-based management or tracking services
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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
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
Abstract
The invention discloses a kind of TDOA-PDR-MAP fusion and positioning methods applied to the complicated interior space.This method feature high in unobstructed indoor scene positioning accuracy using the location technology based on acoustical signal, using the TDOA location estimation based on acoustical signal as the measurement information of particle filter, the cumulative errors of PDR positioning can be effectively made up, and improve the step-size estimation precision in PDR algorithm.It is little that PDR location technology is not easily susceptible to the influence of ambient noise and multipath effect, the position error in short distance, using PDR location model as the state equation of particle filter, can make up for it the deficiency that acoustical signal is located in nlos environment.Meanwhile using the position constraint based on indoor map information, on the one hand positioning result can effectively be corrected, on the other hand certain specific actions can disposably eliminate error, to reduce the cumulative errors of positioning for a long time.Method of the invention is positioned with list TDOA, the positioning accuracy of list PDR positioning is compared, and has higher precision under identical environment.
Description
Technical field
The invention belongs to indoor positioning fields, and in particular to a kind of TDOA-PDR-MAP applied to the complicated interior space melts
Close localization method.
Background technique
In recent years, with using 4G as representative network communication technology, using Beidou as the satellite positioning tech of representative, with intelligence
Mobile phone be representative intelligent mobile wearable device fast development, location based service be more and more widely used in
The daily lifes scene such as trip, shopping, travelling, lodging of the mankind.However, location based service is by by technology and cost institute
Limit, some concentrate on the scene of outdoor positioning, and precision is lower, differ for 1m~10m grades, although the positioning accuracy having is high, at
This is also high, is not suitable for the positioning scene applied to consumer level.And location-based service under indoor environment and be accurate to 1m grades with
Under high accuracy positioning service, although party in request market is huge, the relevant technologies there is also various bottlenecks, such as multipath effect,
Non line of sight measurement, construction cost and transmission range etc..
In the indoor positioning technologies of known degree of precision, the location technology propagation distance based on bluetooth is short, it is big to need
Amount arrangement base station, so that positioning cost increases with space enlargement.Location technology algorithm based on Wi-Fi is complex, difficult
To promote the real-time of Wi-Fi positioning while reducing ambient noise interference.Location technology based on ultra wide band, although precision
It is high, but its high cost apply it can not in consumer level positioning scene.And the indoor positioning technologies tool based on acoustical signal
There is the characteristics of at low cost, easy to operate, operand is small, precision is high, mobile device good compatibility, is highly suitable to be applied for consumer level
Indoor positioning scene.But in complicated indoor environment, due to the influence of building blocked with ambient noise, acoustical signal vulnerable to
The influence of the multipath effect of signal and non-line-of-sight propagation is since acoustical signal spread speed itself is slow, when target movement speed is very fast
When, time delay is also based on one of the problem of acoustical signal positions.Location technology based on PDR need to only be set based on general movement
For without additional base stations, cost is relatively low, is not easily susceptible to the influence of ambient noise and multipath effect, determines in short distance
Position error is little, but can generate biggish accumulated error when used for a long time.And the position constraint based on indoor map information, one
Aspect can effectively correct positioning result, and on the other hand certain specific actions can disposably eliminate error, meanwhile,
Also it can use known cartographic information to identify nlos environment, but cartographic information can not be individually used for target and determine
Position.
Based on above-mentioned status, it can be found that all kinds of methods applied to high-precision indoor positioning field are all respectively present at present
Certain advantage and deficiency.How multipath effect is reduced in position fixing process, non line of sight measures and transmission range is to positioning result
Influence, while reduce application construction cost, improve application privacy, so that it is more applicable for consumer level scene, become
Present invention seek to address that the problem of.
Summary of the invention
In view of the above deficiencies, the present invention provides a kind of TDOA-PDR-MAP fusion positioning side applied to the complicated interior space
Method.The feature that this method makes full use of the location technology based on acoustical signal high in unobstructed indoor scene positioning accuracy, by base
In measurement information of the TDOA location estimation as particle filter of acoustical signal, the cumulative errors of PDR positioning can be effectively made up, and
And the step information in estimation PDR model can assisted.Meanwhile environment is not easily susceptible to using the location technology based on PDR and is made an uproar
The little feature of the influence of sound and multipath effect, the position error in short distance, using PDR location model as particle filter
State equation can make up for it the deficiency that acoustical signal is located in nlos environment.Meanwhile utilizing the position based on indoor map information
Constraint, on the one hand can effectively correct positioning result, and on the other hand certain specific actions can disposably eliminate mistake
Difference, it is also possible to be identified using known cartographic information to nlos environment.
The technical solution adopted in the present invention is as follows: a kind of TDOA-PDR-MAP fusion applied to the complicated interior space is fixed
Position method, comprising the following steps:
Step (1) is carried out acoustical signal with fixed beacon node and is interacted using smart phone as destination node, acquisition sound letter
Number TDOA is measured, and utilizes the bias compensation estimation target position measured based on TDOA；
Step (2) can be read using fabric structure by the data structure foundation of vector sum figure layer hybrid representation at any time
Take the electronic map with operation；
Step (3), using PDR motion model as system state equation, using TDOA acoustical signal positioning result as system quantities
Measured value positions target using particle filter blending algorithm using electronic map information as the constraint of target motion path.
Further, the concrete operations of the step (1) are as follows:
Defining target position estimated value isBeaconing nodes number is ns, wherein the seat of i-th of beaconing nodes
It is designated as Si=(sxi,syi), nsA beaconing nodes sounding in turn chooses beaconing nodes that number is j as reference mode.It will compile
The reference mode range difference measuring value that number beaconing nodes for being i and number are j is denoted as Δ dij, by number for i beaconing nodes and
The reference mode range difference true value that number is j is denoted as dij, will number the reference mode for being j for the beaconing nodes and number of i away from
The measurement noise of deviation is denoted as εij, εijIt obeysZero-mean gaussian distribution, σijIt is the measurement noise with environmental correclation
Variance has Δ dij=dij+εij；It chooses the beaconing nodes that number is 1 and acoustical signal is obtained according to above-mentioned relation as reference mode
The model that TDOA is measured:
TDOA measure bias compensation the target position in acoustical signal TDOA measurement model is estimated, the algorithm
Include the following steps:
Step (A1): according to acoustical signal TDOA measurement model, augmented matrix H=[- G is constructed1 h1], wherein
Step (A2): according to noise is measured, Δ H=H-H is obtained0, wherein Η0For the true value of matrix H, Δ H is matrix H
Error in measurement item；
Step (A3): constraint matrix Ω=E { Δ H of deviation is obtained according to step (A2)TW1Δ H }, wherein W1To make to be
The weighting matrix that error in measurement of uniting minimizes, when the constraint matrix Ω of deviation only has the submatrix in the lower right corner to be non-zero matrix,
It is denoted as
Step (A4): by HTW1H is split according to the structure of the constraint matrix Ω of step (A3) large deviations, obtains 2*2's
Matrix in block form:
Wherein, A11、A12、A22For the submatrix of the constraint matrix Ω of deviation；
Step (A5): ν is defined1、ν2For preliminary aim location estimation solution z1The component of estimation, λ are ginseng relevant to signal-to-noise ratio
Number, is solved by following formula
Step (A6): ν is solved by following formula1:
Step (A7): preliminary aim location estimation solution z is obtained according to step (A5), step (A6)1, wherein L is vector ν2's
Length:
Step (A8): by the preliminary aim location estimation solution z of step (A7)1Bring second of location estimation solution z intoa, wherein
Ga, h be parameter matrix, Ψ z1Error co-variance matrix:
Step (A9): second of location estimation solution z of step (A8) is utilizedaUndated parameter matrix and error covariance square
Battle array, updated parameter matrix are denoted as Ga', h', updated error co-variance matrix Ψ '；
Step (A10): weighted least square third time location estimation solution z is utilized againa': za'=(Ga'TΨ'- 1Ga')-1Ga'TΨ'-1h'；
Step (A11): location estimation result is obtained:
Further, the concrete operations of the step (2) are as follows: by the data structure of vector sum figure layer hybrid representation, from
Space (space), line (line), point (point), node (node) these features composition electronics are taken out in fabric structure
Map individually can call and calculate, pass through vector between characteristic quantity wherein each characteristic quantity occupies a figure layer in map
Expression is attached.
Further, the concrete operations of the step (3) are as follows:
(C1) X is definedk=[xk yk]TTrue coordinate for user at the k moment, lkFor the true step-length at k moment, θkWhen for k
The course angle at quarter,It is systematic state transfer noise, using PDR motion model as system state equation:
(C2) using TDOA acoustical signal positioning result as systematic observation equation:Wherein, when remembering k
Carve acoustical signal positioning result beσwFor the observation noise of system；
(C3) particle position and particle step-length are initialized based on TDOA location estimation；
Definition total number of particles is n, wherein state of i-th of particle at the k moment beWeight is
Initialization time window m is set, the initial position at the m moment of target is TDOA location estimation information
By in initial positionOn the basis of zero mean Gaussian white noise is added, obtain the initialization information of particleIf each particle is in the initialization weight at m momentAnd it is obtained using m TDOA location estimation
The initial step length estimated value at the moment:
(C4) particle right value update and target position estimation are carried out based on TDOA measurement information and electronic map information joint；
Weight based on TDOA i-th of the particle measuredAre as follows:WhereinBased on judge particle whether be transferred to completely can not connected space i-th particle
WeightAre as follows:Based on judge particle whether be transferred to currently can not connected space
The weight of i particleAre as follows:Right value update by three weights multiplications as particle
Formula:Then again particle weight is normalized to obtain
Location estimation result of the target at the k moment are as follows:
(C5) work as number of effective particlesWhen less than number of effective particles threshold value, the side based on random resampling
Formula carries out resampling to particle；
(C6) the location estimation result using least square method based on target at the k moment updates the true step of k moment particle
Long lk:
lk=(Al TAl)-1Al TBl
Wherein, Al=[xk-xk-m+1 yk-yk-m+1]T,
(C7) positioning result is constrained and is corrected using electronic map information；When judgement k moment target is worn
When door movement, destination path is constrained using electronic map information, updates location estimation result.
Further, in the step (C7), when determine k moment target have occurred wear movement when, utilize electronic map
Information constrains destination path, updates location estimation as a result, concrete operations are as follows:
When finding that target wears door at the k moment, then positioning result rollback N is walked, even position is estimated in the target position at k-N moment
In the coordinate of door, and in the step of N later, the relative position of the coordinates of targets of the coordinates of targets and back of each step is constant；It is fixed
The coordinate for the door that adopted target passes through is Xd=[xd yd]T, available target position of updated k moment estimation are as follows:
Compared with the existing technology, beneficial effects of the present invention are as follows: complicated indoor fixed present invention firstly provides being suitable for
The fusion location algorithm based on acoustical signal TDOA location estimation, PDR algorithm and electronic map information of potential field scape, improves PDR
Positioning leads to the problem of cumulative errors for a long time, acoustical signal generates very big position error under non line of sight and multi-path environment, positioning
Within precision is up to meter level.
Detailed description of the invention
Fig. 1 is that TDOA-PDR-MAP merges location algorithm structure；
Fig. 2 is electronic map information data structure；
Fig. 3 is system motion modular concept figure；
Fig. 4 is pedestrian movement's track following diagram.
Specific embodiment
The present invention provides a kind of TDOA-PDR-MAP fusion and positioning method applied to the complicated interior space, this method framework
As shown in Figure 1, specifically comprising the following steps.
Step 1, target position is estimated using the bias compensation measured based on TDOA.
In TDOA location model, defining target position estimated value isBeaconing nodes number is ns, wherein
The coordinate of i-th of beaconing nodes is Si=(sxi,syi), nsA beaconing nodes sounding in turn chooses the beaconing nodes that number is j
As reference mode.The reference mode range difference measuring value for being j for the beaconing nodes and number of i will be numbered and be denoted as Δ dij, will compile
The reference mode range difference true value that number beaconing nodes for being i and number are j is denoted as dij, beaconing nodes and volume for i will be numbered
Number ε is denoted as the measurement noise of reference mode range difference of jij, εijIt obeysZero-mean gaussian distribution, σijIt is and ring
The relevant measuring noise square difference in border, there is Δ dij=dij+εij.It chooses and numbers the beaconing nodes for being 1 as reference mode according to above-mentioned
Relationship obtains acoustical signal TDOA measurement model:
The error in measurement vector of system isAssuming that each error vector is the Gauss being independently distributed
Noise εi1~N (0, σ2), then error in measurement vector covariance matrix Q are as follows:
Acoustical signal TDOA measurement model is arranged to obtain: B1N=h1-G1z1
Wherein, parameterz1For the true value of position estimation value, n is to measure noise square
Battle array, two parameter matrixs of system are as follows, wherein XiFor the coordinate of i-th of sensor:
TDOA measure bias compensation the target position in acoustical signal TDOA measurement model is estimated, the algorithm
Include the following steps:
Step (A1): according to acoustical signal TDOA measurement model, augmented matrix H=[- G is constructed1 h1]；
Step (A2): according to noise is measured, Δ H=H-H is obtained0, wherein Η0For the true value of matrix H, Δ H is matrix H
Error in measurement item；
Step (A3): constraint matrix Ω=E { Δ H of deviation is obtained according to step (A2)TW1Δ H }, wherein W1To make to be
The weighting matrix that error in measurement of uniting minimizes, when the constraint matrix Ω of deviation only has the submatrix in the lower right corner to be non-zero matrix,
It is denoted as
Step (A4): by HTW1H is split according to the structure of the constraint matrix Ω of step (A3) large deviations, obtains 2*2's
Matrix in block form:
Wherein, A11、A12、A22For the submatrix of the constraint matrix Ω of deviation；
Step (A5): ν is defined1、ν2For preliminary aim location estimation solution z1The component of estimation, λ are ginseng relevant to signal-to-noise ratio
Number, is solved by following formula
Step (A6): ν is solved by following formula1:
Step (A7): preliminary aim location estimation solution z is obtained according to step (A5), step (A6)1, wherein L is vector ν2's
Length:
Step (A8): by the preliminary aim location estimation solution z of step (A7)1Bring second of location estimation solution z intoa, wherein
Ga, h be parameter matrix, Ψ z1Error co-variance matrix:
Step (A9): second of location estimation solution z of step (A8) is utilizedaUndated parameter matrix and error covariance square
Battle array, updated parameter matrix are denoted as Ga', h', updated error co-variance matrix Ψ '；
Step (A10): weighted least square third time location estimation solution z is utilized againa': za'=(Ga'TΨ'- 1Ga')-1Ga'TΨ'-1h'；
Step (A11): location estimation result is obtained:
Step 2, indoor electronic map is modeled using fabric structure.
Cartographic information is constructed by the data structure of vector sum figure layer hybrid representation, is convenient in subsequent algorithm design to each
Class cartographic information is inquired, called and is calculated.Space (space), line are taken out from necessary cartographic information herein
(line), point (point), node (node) these features, each characteristic quantity occupy a figure layer in map, can be independent
It calls and calculates, be attached between characteristic quantity by vector representation.Data relationship between several features is expressed as data
Structure chart is as shown in the figure.Each characteristic quantity is described as follows:
(1) map (Map).Each map is made of the set in space, when being positioned, it is believed that pedestrian is only giving
It is movable in fixed map.
(2) space (space).Space is the unit for constituting map, can be at it by pedestrians such as room, corridor, staircases
In free-moving Environment Definition be space.The characteristic attribute in space includes: space number (Space_id), spatial connectivity
(Connect), the set of the line set (Line) in space, the node for acoustical signal positioning for including in space is formed
(Node)。
(3) line (line).Line is the unit for constituting space, and the characteristic variable that door and two class of wall are separated different spaces is fixed
Justice is line, and the line of most trifle is taken to be described.The characteristic attribute of line includes: line number (Line_id), whether attribute is door
(Door), the set (Points) of starting point coordinate and terminal point coordinate.
(4) point (point).Point is splice point, turning point and other points of interest manually demarcated all in space.Point
Characteristic attribute include: number (Point_id), abscissa (x), ordinate (y).
(5) node (node).Node is the beaconing nodes for being previously placed at each space, positioning for TDOA acoustical signal,
The position of beaconing nodes is as cartographic information, under normal circumstances once will not change for a long time after arrangement, therefore also by it
It is put into map data structure.The characteristic attribute of node includes: number (Node_id), abscissa (x), ordinate (y).
Step 3, target is positioned using TDOA-PDR-MAP particle filter blending algorithm.
In this step, the frame based on particle filter, using PDR motion model as system state equation, with TDOA sound letter
Number positioning result is as system quantities measured value, using electronic map information as the constraint of target motion path.Specific algorithm step is such as
Under:
(1) system state equation is set
PDR motion model schematic diagram is as shown, using the model as system state equation:
Wherein, Xk=[xk yk]TTrue coordinate for user at the k moment, lkFor the true step-length at the moment, θkWhen for this
The course angle at quarter, systematic state transfer noise are zero-mean gaussian distributions: σc~N (0, σc 2)。
(2) systematic observation equation is set.
Using TDOA acoustical signal positioning result as systematic observation equation:
Wherein, the positioning result for remembering acoustical signal isV (k)=[σwσw]TFor the observation noise of system
Matrix, σwFor the observation noise of system.
(3) particle position and particle step-length are initialized based on TDOA location estimation.
If particle state space are as follows: Pi=[xi yi]T, i=1,2 ... wherein, n is total number of particles, (x to ni, yi) it is i-th
Position coordinates of a particle in x-axis and y-axis.Initialization time window m is set, the initial position at the m moment of target is
TDOA location estimation informationBy in initial positionOn the basis of zero mean Gaussian white noise is added,
Obtain the initialization information of particleAnd each particle is set in the initialization weight at m momentThen the initial step length estimated value at the moment is obtained using m TDOA location estimation:
(4) particle right value update and target position estimation are carried out based on TDOA measurement information and electronic map information joint.
The right value update formula of particle by measured based on TDOA particle weight formula, based on completely can not connected space sentence
It is disconnected, based on currently can not connected space judge three weight formula compositions.
Meaning based on the TDOA particle weight measured is by increasing the weight grain with authentic communication that retains those
Son, and insecure particle is eliminated by reducing weight.The reliability of particle judged at a distance from observation by it, and
Observed quantity is apart from close particle high reliablity, it should improve its weight；Remote with observed quantity distance should then reduce its weight.Base
In the weight for i-th of particle that TDOA is measuredAre as follows:
Wherein
When the line for not including " door " attribute in the line set for including in space, then determine that the space is that can not be connected to sky completely
Between.Using binaryzation Rule of judgment, if the coordinate fall in completely can not connected space, this index of particle is directly set 0,
Based on judge particle whether be transferred to completely can not connected space i-th of particle weightAre as follows:
When i-th of particle the k moment carry out a next state transfer after, state transfer after coordinate where space and grain
Son is between the space where the location estimation at k-1 moment, the line not comprising common " door " attribute, then determines that the space is to work as
Before can not connected space.Using binaryzation Rule of judgment, if particle fall in currently can not connected space, directly by particle should
Index sets 0, based on judge particle whether be transferred to currently can not connected space i-th of particle weightAre as follows:
Right value update formula by three weights multiplications as particle:Then again to particle weight
It is normalized to obtain:
Location estimation result of the target at the k moment are as follows:
(5) particle resampling.
Work as number of effective particlesWhen less than number of effective particles threshold value, the mode pair based on random resampling
Particle carries out resampling.Method particularly includes: n particle weight after right value update is arranged in [0,1] section, each particle
Shared siding-to-siding block length is equal with its weight, and is called the weight section of the particle.Then n are taken between [0,1] section
Random number, for each random number, the corresponding particle in the section is found in the weight section fallen in by each random number, and should
Particle copies to new particle collection.New particle is concentrated, and the weight of each particle is 1/n, and particle property still conform to it is old
The distribution of particle collection.
(6) particle step-length updates.
Location estimation result using least square method based on target at the k moment updates the true step-length l of k moment particlek:
lk=(Al TAl)-1Al TBl
Wherein, Al=[xk-xk-m+1 yk-yk-m+1]T,
(7) positioning result is constrained and is corrected using electronic map information.
When determine k moment target have occurred wear movement when, destination path is constrained using electronic map information.Tool
Body method are as follows: when finding that target wears door at the k moment, then walk positioning result rollback N, even the target position at k-N moment is estimated
Positioned at the coordinate of door, and in the step of N later, the relative position of the coordinates of targets of the coordinates of targets and back of each step is constant.
The moment target position k estimation after being corrected are as follows:
Main contributions of the invention are: the location algorithm is positioned with list TDOA, the positioning accuracy of list PDR positioning is compared,
There is higher precision under identical environment.But at the same time, the quality of locating effect is dependent on following several in implementation process
A factor: first is that ambient noise, due to sending the noise of specific frequency in acoustical signal position fixing process by beaconing nodes, when environment is made an uproar
When noise in sound containing the frequency range, positioning accuracy be will be greatly reduced.Second is that the accuracy of electronic map information building, due to calculating
Cartographic information is introduced in method as constraint condition, when cartographic information inaccuracy, be will cause algorithm erroneous judgement, is influenced positioning accurate
Degree.Third is that the sensor accuracy of the mobile devices such as mobile phone, since the PDR Correlated Case with ARMA Measurement information in algorithm is the individual by user
What mobile device obtained, therefore the sensor accuracy difference of mobile device will also result in different position errors.
Application environment
Currently invention addresses consumer level positioning scenes, are focusing on being additionally contemplates that construction cost while positioning accuracy is promoted
Protected with privacy of user, suitable for known to cartographic information, beaconing nodes can be placed, request for location services is actively initiated by user
And need the complex indoor scene of high accuracy positioning.Such as retail shop's navigation of shopping mall, the automatic seeking vehicle in parking lot, reality
The player of scape game guides, the robot of express delivery website divides personal management of storehouse and enclosure space etc..
Claims (5)
1. a kind of TDOA-PDR-MAP fusion and positioning method applied to the complicated interior space, which is characterized in that including following step
It is rapid:
Step (1) is carried out acoustical signal with fixed beacon node and interacted, obtained acoustical signal using smart phone as destination node
TDOA is measured, and estimates target position using the bias compensation measured based on TDOA；
Step (2), using fabric structure, can be read at any time by the data structure foundation of vector sum figure layer hybrid representation and
The electronic map of operation；
Step (3), using PDR motion model as system state equation, using TDOA acoustical signal positioning result as system quantities measured value,
Using electronic map information as the constraint of target motion path, target is positioned using particle filter blending algorithm.
2. the method according to claim 1, wherein the concrete operations of the step (1) are as follows:
Defining target position estimated value isBeaconing nodes number is ns, wherein the coordinate of i-th of beaconing nodes is Si
=(sxi,syi), nsA beaconing nodes sounding in turn chooses beaconing nodes that number is j as reference mode.It will number as i's
The reference mode range difference measuring value that beaconing nodes and number are j is denoted as Δ dij, will number as the beaconing nodes and number of i is j
Reference mode range difference true value be denoted as dij, the amount for the reference mode range difference for being j for the beaconing nodes and number of i will be numbered
It surveys noise and is denoted as εij, εijIt obeysZero-mean gaussian distribution, σijIt is the measuring noise square difference with environmental correclation, there is Δ
dij=dij+εij；It chooses the beaconing nodes that number is 1 and obtains what acoustical signal TDOA was measured according to above-mentioned relation as reference mode
Model:
The bias compensation that TDOA is measured estimates that the target position in acoustical signal TDOA measurement model, the algorithm includes
Following steps:
Step (A1): according to acoustical signal TDOA measurement model, augmented matrix H=[- G is constructed1 h1], wherein
Step (A2): according to noise is measured, Δ H=H-H is obtained0, wherein Η0For the true value of matrix H, Δ H is the amount of matrix H
Survey error term；
Step (A3): constraint matrix Ω=E { Δ H of deviation is obtained according to step (A2)TW1Δ H }, wherein W1To make system quantities
The weighting matrix minimized the error is surveyed, when the constraint matrix Ω of deviation only has the submatrix in the lower right corner to be non-zero matrix, is denoted as
Step (A4): by HTW1H is split according to the structure of the constraint matrix Ω of step (A3) large deviations, obtains the piecemeal of 2*2
Matrix:
Wherein, A11、A12、A22For the submatrix of the constraint matrix Ω of deviation；
Step (A5): ν is defined1、ν2For preliminary aim location estimation solution z1The component of estimation, λ are parameter relevant to signal-to-noise ratio,
It is solved by following formula
Step (A6): ν is solved by following formula1:
Step (A7): preliminary aim location estimation solution z is obtained according to step (A5), step (A6)1, wherein L is vector ν2Length
Degree:
Step (A8): by the preliminary aim location estimation solution z of step (A7)1Bring second of location estimation solution z intoa, wherein Ga、h
For parameter matrix, Ψ z1Error co-variance matrix:
Step (A9): second of location estimation solution z of step (A8) is utilizedaUndated parameter matrix and error co-variance matrix update
Parameter matrix afterwards is denoted as Ga', h', updated error co-variance matrix Ψ '；
Step (A10): weighted least square third time location estimation solution z is utilized againa':
za'=(Ga'TΨ'-1Ga')-1Ga'TΨ'-1h'
Step (A11): location estimation result is obtained:
3. the method according to claim 1, wherein the concrete operations of the step (2) are as follows: pass through vector sum figure
The data structure of layer hybrid representation, takes out space (space), line (line), point (point), node from fabric structure
(node) these features constitute electronic map and can individually call wherein each characteristic quantity occupies a figure layer in map
And calculating, it is attached by vector representation between characteristic quantity.
4. the method according to claim 1, wherein the concrete operations of the step (3) are as follows:
(C1) X is definedk=[xk yk]TTrue coordinate for user at the k moment, lkFor the true step-length at k moment, θkFor the k moment
Course angle, σc~N (0, σc 2) it is systematic state transfer noise, using PDR motion model as system state equation:
(C2) using TDOA acoustical signal positioning result as systematic observation equation:Wherein, note k moment sound letter
Number positioning result beσwFor the observation noise of system；
(C3) particle position and particle step-length are initialized based on TDOA location estimation；
Definition total number of particles is n, wherein state of i-th of particle at the k moment beWeight isSetting
Initialization time window m, the initial position at the m moment of target are TDOA location estimation informationPass through
In initial positionOn the basis of zero mean Gaussian white noise is added, obtain the initialization information of particle
If each particle is in the initialization weight at m momentAnd the initial step at the moment is obtained using m TDOA location estimation
Long estimated value:
(C4) particle right value update and target position estimation are carried out based on TDOA measurement information and electronic map information joint；
Weight based on TDOA i-th of the particle measuredAre as follows:WhereinBased on judge particle whether be transferred to completely can not connected space i-th of particle power
ValueAre as follows:Based on judge particle whether be transferred to currently can not connected space i-th
The weight of a particleAre as follows:Three weights are multiplied public as the right value update of particle
Formula:Then again particle weight is normalized to obtain
Location estimation result of the target at the k moment are as follows:
(C5) work as number of effective particlesWhen less than number of effective particles threshold value, the mode based on random resampling is to grain
Son carries out resampling；
(C6) the location estimation result using least square method based on target at the k moment updates the true step-length l of k moment particlek:
lk=(Al TAl)-1Al TBl
Wherein, Al=[xk-xk-m+1 yk-yk-m+1]T,
(C7) positioning result is constrained and is corrected using electronic map information；When determining that k moment target has occurred that wear door dynamic
When making, destination path is constrained using electronic map information, updates location estimation result.
5. the method according to claim 1, wherein in the step (C7), when determining that k moment target has occurred
When wearing movement, destination path is constrained using electronic map information, updates location estimation as a result, concrete operations are as follows: when
It finds that target wears door at the k moment, then walks positioning result rollback N, even the target position estimation at k-N moment is located at the seat of door
Mark, and in the step of N later, the relative position of the coordinates of targets of the coordinates of targets and back of each step is constant；Target is defined to wear
The coordinate for the door crossed is Xd=[xd yd]T, available target position of updated k moment estimation are as follows:
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