CN108632761A - A kind of indoor orientation method based on particle filter algorithm - Google Patents
A kind of indoor orientation method based on particle filter algorithm Download PDFInfo
- Publication number
- CN108632761A CN108632761A CN201810361226.2A CN201810361226A CN108632761A CN 108632761 A CN108632761 A CN 108632761A CN 201810361226 A CN201810361226 A CN 201810361226A CN 108632761 A CN108632761 A CN 108632761A
- Authority
- CN
- China
- Prior art keywords
- fix
- elements
- algorithms
- wifi
- mag
- 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.)
- Granted
Links
- 239000002245 particle Substances 0.000 title claims abstract description 126
- 238000000034 method Methods 0.000 title claims abstract description 61
- 230000005389 magnetism Effects 0.000 claims abstract description 72
- 239000013598 vector Substances 0.000 claims description 32
- 230000033001 locomotion Effects 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 15
- 230000004927 fusion Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000012952 Resampling Methods 0.000 claims description 8
- 238000010606 normalization Methods 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 claims description 4
- 239000011164 primary particle Substances 0.000 claims description 4
- 230000032258 transport Effects 0.000 claims description 4
- HUTDUHSNJYTCAR-UHFFFAOYSA-N ancymidol Chemical compound C1=CC(OC)=CC=C1C(O)(C=1C=NC=NC=1)C1CC1 HUTDUHSNJYTCAR-UHFFFAOYSA-N 0.000 abstract description 4
- 238000012795 verification Methods 0.000 abstract 1
- 238000001914 filtration Methods 0.000 description 8
- 238000012360 testing method Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 230000004807 localization Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 241000272201 Columbiformes Species 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 241000238565 lobster Species 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- 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
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
-
- 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
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
A kind of indoor orientation method based on particle filter algorithm, it is merged with Geomagnetic signal three-dimensional series using navigate position information, WiFi signal strength information of pedestrian of the user after starting walking in N steps, determine that initial point region, earth magnetism are accurately positioned determining initial position co-ordinates by WiFi, recycle the independence of the positioning result of the arest neighbors matching algorithm based on WiFi, the particle filter algorithm based on WiFi and PDR and the particle filter algorithm based on earth magnetism and PDR, mutually verification, so that positioning is kept tracking, improves the robustness of positioning.This method suitable for flush end, is swung one's arm, four kinds of pocket, knapsack mobile phones placement patterns, and positioning accuracy is high under the premise of obtaining more accurate pedestrian's boat position information (including meter step and pedestrian direction).
Description
Technical field
The present invention relates to indoor positioning tracking field more particularly to a kind of indoor positioning sides based on particle filter algorithm
Method.
Background technology
With the fast development of present mobile communication technology, location based service LBS (Location Based
Services people) have been obtained more and more to favor, low cost has been realized and high-precision indoor positioning algorithms receives very much
The concern of researcher.Now, it is based on the interior of WiFi, infrared ray, bluetooth, ultra wide band broadcast (UWB), earth magnetism, inertial sensor
Localization method has obtained research circle and has widely paid close attention to, but ultra wide band broadcast (UWB), infrared technique carry out positioning and is required for again
Deployment facility, it is expensive.
And in recent years, with the fast development of WLAN (WiFi), all widespread deployment in most of indoor environments
WiFi network, i.e., arrange AP extensively, and the smart machine at different location, which passes through, receives signal carry out office from different AP
The connection of domain net, i.e., smart machine can receive the signal from different AP at different locations, or receive identical AP's
Signal but its signal strength values can have differences.Therefore, the received signal strength of AP can be regarded as to a kind of location fingerprint progress
Indoor positioning.
On the other hand, pathfinding can be carried out using earth magnetic field by the biology such as homing pigeon, lobster in biology to be inspired, ground
Study carefully personnel to find that indoor positioning can be carried out using earth magnetic field.Indoors under environment, earth magnetic field by building reinforcing bar
The influence of concrete structure, internal pipeline cable and large-scale electromagnetic equipment etc. and be distorted, to cause indoor earth's magnetic field
Uneven distribution, it is possible to regard earth's magnetic field as a kind of location fingerprint and carry out indoor positioning.
It is suitable for static immobilization using the indoor positioning technologies of WiFi fingerprints or earth magnetism fingerprint, in real-time positioning and tracking
It cannot play a role well.And inertial navigation technology is a kind of entirely autonomous navigation locating method, utilizes smart mobile phone collection
At the sensors such as accelerometer, gyroscope, electronic compass, magnetometer measurement data, carry out inspection step, step-size estimation, course
Angular estimation, therefore pedestrian's dead reckoning (Pedestrian Dead Reckoning, PDR) algorithm can be used to carry out pedestrian's rail
The positioning of mark.But PDR algorithms need to know accurate initial position, and in practice, the more difficult acquisition of initial position；Due to used
The error of property sensor, positioning accuracy is higher within a short period of time, as the time increases, it may appear that larger accumulated error.
In existing scheme, using based on WiFi and PDR particle filter algorithm (WiFi-PF) or based on earth magnetism with
The particle filter algorithm (Mag-PF) of PDR may be implemented dynamic and position, and obtain continuous user's run trace.But WiFi-PF
Or Mag-PF algorithms are susceptible to positioning runout pedestrian's real trace, locating and tracking failure after prolonged locating and tracking.Especially
It is Mag-PF algorithms, and since earth magnetism fingerprint is three-dimensional vector, space uniqueness is poor, once there is positioning runout actual position,
The earth magnetism sequences match that the earth magnetism fingerprint sequence of particle may still be arrived with Current observation, to estimate the elements of a fix, and algorithm sheet
The positioning of body None- identified has failed.And the advantages of fingerprint matching algorithm of WiFi is that front and back positioning result twice is mutual indepedent,
And position error has certain range, therefore the independence of the fingerprint matching algorithm using WiFi-PF, Mag-PF and WiFi,
It in the positioning often walked, mutually verifies, is merged that positioning is made to keep correct tracking.
Invention content
Present invention is generally directed to existing particle filter algorithm of the exclusive use based on WiFi and PDR and based on earth magnetism with
The particle filter algorithm of PDR is susceptible to the problem of locating and tracking failure, and it is an object of the present invention to provide a kind of being based on particle filter algorithm
Indoor orientation method, this method using the particle filter algorithm based on WiFi and PDR, the particle filter based on earth magnetism and PDR
Algorithm and independence based on the fingerprint matching algorithm of WiFi in positioning mutually verify, and positioning is made to keep tracking, improve positioning
Robustness.
To achieve the above object, the technical solution adopted by the present invention is as follows：
A kind of indoor orientation method based on particle filter algorithm, includes the following steps：Using initial position fix algorithm
It determines initial point coordinates, then realizes that positioning, detailed process are as follows by merging the particle filter algorithm of PDR, WiFi and earth magnetism：
Step 1：With L0(x0,y0) it is the particle initialization that initial point carries out WiFi-PF algorithms and Mag-PF algorithms respectively；
Step 2：Real-time estimation user walking step number and direction are carried out using PDR algorithms, PDR algorithms are detected
Kth walks, and obtains the elements of a fix of corresponding WiFi-PF algorithmsThe elements of a fix of Mag-PF algorithmsAnd the elements of a fix of WiFi-KNN algorithms
Step 3：When kth walks, whether the elements of a fix by merging Mag-PF algorithms carry out locating and tracking with cartographic information
Failure judges；
Step 4：It is reference with the elements of a fix of WiFi-PF algorithms if judging the locating and tracking failure of Mag-PF algorithms
Particle is reinitialized, i.e., with the elements of a fix of the WiFi-PF of current kth stepFor the center of circle, R is half
Diameter draws circle, generates Num particle at random in circle, and instead of primary particle collection, particle filter is carried out as next step Mag-PF algorithms
Particle collection, then carry out step 6；If judging, the locating and tracking of Mag-PF algorithms is normal, carries out step 5；
Step 5：When kth walks, by the elements of a fixWith the elements of a fix
It is merged, obtaining the elements of a fix after WiFi-PF algorithms are merged with the elements of a fix of Mag-PF algorithms is
Step 6：Using the elements of a fix of WiFi-PF algorithms as the elements of a fix after fusion, i.e.,
Step 7：Calculate the elements of a fix of WiFi-KNN algorithmsWith merge after the elements of a fixThe distance betweenIf the elements of a fix of WiFi-KNN algorithmsWith merge after the elements of a fixThe distance between diskMore than or equal to thresholding δ, then say
Bright WiFi-PF algorithms fail with Mag-PF algorithm locating and trackings, distinguish the particle collection in WiFi-PF algorithms and Mag-PF algorithms
It is handled, increases a part and be distributed in positionThe random particles of surrounding, more new particle collection, then updating
Particle afterwards works in+1 step of kth, the elements of a fix currently walkedIf WiFi-KNN is calculated
The elements of a fix of methodWith merge after the elements of a fixThe distance between diskLess than door
δ is limited, then willAs the elements of a fix currently walked
Step 8：If detecting, meter step, k=k+1 jump to step 3；Otherwise, positioning terminates.
The present invention, which further improves, to be, initialization detailed process is in step 1)：With L0(x0,y0) it is the center of circle, R is
Radius draws circle, Num particle of random distribution in circle, as primary collection, wherein calculates WiFi-PF algorithms and Mag-PF
Method generates respective primary collection.
The present invention, which further improves, to be, it is as follows that the detailed process whether locating and tracking unsuccessfully judges is carried out in step 3：
It detects in 20 steps before kth step, the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) enter unreachable region
Number RIM-PF, i.e. the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) enter behind unreachable region start again it is initial
The number RI of changeM-PF, compare the positioning result of Mag-PF into the number RI in unreachable regionM-PFWith thresholding TM-PFRelationship, if
RIM-PF>TM-PF, then illustrate the locating and tracking failure of Mag-PF algorithms；Otherwise, illustrate that the locating and tracking of Mag-PF algorithms is normal.
The present invention, which further improves, to be, it is as follows to start the detailed process reinitialized：
1. particle initializes：For the elements of a fix before being walked using n as the center of circle, R is that radius draws circle, random in circle to generate Num
The step number walked in the same direction before particle, wherein kth step is p, then
2. state shifts：The direction of Particles Moving be n step in mean direction, step-length be n times often grow step by step；
3. observation：Pedestrian collected earth magnetism three-dimensional series B in n is walkednow；
4. right value update：Movement locus corresponding earth magnetism three-dimensional series B of i-th of particle in n stepsi, transported according to particle
The Origin And Destination coordinate of dynamic estimation carries out linear interpolation by earth magnetism fingerprint in database to be obtained；Calculate the ground of i-th of particle
Magnetic three-dimensional series BiWith observation BnowBetween DTW distances be denoted as Di, then the weight of i-th of particle beThen to power
It is normalized again, wherein σD 2Indicate the variance of DTW distances；
5. resampling：Carry out simple randomization resampling；
6. location estimation：It is averaged to the position of all particles, obtains the elements of a fix of kth step.
The present invention, which further improves, to be, the detailed process of step 5 is：According to the elements of a fixWith the WiFi-KNN algorithm elements of a fixBetween distanceThe elements of a fixWith the elements of a fixBetween distanceBy the elements of a fixAnd the elements of a fixIt is weighted to obtain the elements of a fix
The two weighting weight be respectivelyWherein,For the elements of a fixWeight,For the elements of a fixWeight, ωkFor parameter.
The present invention, which further improves, to be, parameter ωkCalculation there are two types of：
1. counting backward technique：
2. index method：
The present invention further improve is, WiFi-PF algorithms merged with the elements of a fix of Mag-PF algorithms after positioning
Coordinate isCalculation formula is
The present invention, which further improves, to be, the detailed process of initial point coordinates is determined such as using initial position fix algorithm
Under：
Step 1：Pedestrian starts the N steps after walking, is counted to the acceleration of smart mobile phone by pedestrian's dead-reckoning algorithms
According to, gyro data, electronic compass data and the magnetometer data direction that estimates the step number of pedestrian's walking and often walk；
Step 2：Initial point region is determined using WiFi, within the N step times that pedestrian's starting row is walked, the WiFi of smart mobile phone
Scan module carries out periodical WiFi signal scanning, therefore collects the signal strength vector of multiple WiFi, to multiple WiFi's
Signal strength vector carries out average value processing, obtains the WiFi average signal strength vectors in this time, utilizes nearest neighbor algorithm
It is positioned, obtains positioning resultThen it sets with positioning result initial point region toFor the center of circle, R0For radius
Circle；
Step 3：It is accurately positioned using earth magnetism three-dimensional series data, determines that initial point coordinates, principle are to utilize maximum
Posterior estimator criterion, by observation come the posterior probability of sample estimates, to estimate initial point coordinates.
The present invention, which further improves, to be, estimates that the detailed process of initial point coordinates is as follows in step 3：
1. in the initial point region that step 2 determines, generates and obey equally distributed NumI sample, sample position set
For
{pi=(xi,yi), i=1,2 ..., NumI }；
2. each sample i is according to step number N, the step-length d estimated in step 1iAnd the mean direction in preceding N steps directionIt transports forward
Dynamic, movement locus is denoted as li, i-th of sample equation of motion be
Wherein, step-length diWith direction of motion θiIt is all added to Gaussian noise, d indicates every and grows step by step,Indicate N long Nd step by step
Variance,Indicate mean directionVariance；
For i-th of sample, its movement locus l is obtainediCorresponding earth magnetism three-dimensional vector sequence Mi, earth magnetism three-dimensional vector sequence
Arrange MiIt is obtained by earth magnetism fingerprint linear interpolation in database；
3. observation is collected earth magnetism three-dimensional vector sequence M in preceding N stepsnow, pass through MnowAfter calculating i-th of sample
Test probability Pi, the posterior probability calculation of i-th of sample is as follows：Calculate the corresponding earth magnetism three-dimensional vector sequence M of i-th of samplei
Collected earth magnetism sequence M in being walked with preceding NnowBetween DTW distances be denoted as Di, then the weight of i-th of sample beThen
Posterior probability is the value after weight normalizationWhereinσD 2Indicate the variance of DTW distances；
4. selecting the maximum K sample of posterior probability, and carry out 3 σ criterion rejecting abnormalities samples, remaining K0A sample { pj=
(xj,yj), j=1,2 ..., K0, finally to K0The position coordinates of a sample carry out average value processing, calculate initial point coordinates
Compared with prior art, the device have the advantages that：The present invention is calculated using the arest neighbors matching based on WiFi
Method (WiFi-KNN), the particle filter algorithm (WiFi-PF) based on WiFi and PDR and based on the particle filter of earth magnetism and PDR calculate
The independence of the positioning result of method (Mag-PF) mutually verifies, and so that positioning is kept tracking, improves the robustness of positioning.Fusion
The key technology of the particle filter algorithm of PDR, WiFi and earth magnetism includes mainly：WiFi-PF merges plan with Mag-PF positioning results
Slightly, self inspection policies that fail, WiFi-KNN auxiliary judgments positioning failure plan are positioned based on the particle filter algorithm of earth magnetism and PDR
Slightly, particle reinitializes strategy, and Mag-PF reinitializes strategy.The system is obtaining more accurate pedestrian's boat position information
Under the premise of (including meter step and pedestrian direction), suitable for flush end, swing one's arm, four kinds of pocket, knapsack mobile phones placement patterns, and position
Precision is high.This method utilizes the particle filter algorithm based on WiFi and PDR, the particle filter algorithm based on earth magnetism WiFi and PDR
And the independence based on the fingerprint matching algorithm of WiFi in positioning, it mutually verifies, so that positioning is kept tracking, improve the Shandong of positioning
Stick.
Description of the drawings
Fig. 1 is accurate initial position fix algorithm flow chart；
Fig. 2 is the algorithm flow chart for the particle filter algorithm for merging PDR, WiFi and earth magnetism；
Fig. 3 is the plan view of test environment.
Specific implementation mode
Make in order to illustrate the technical solution of the embodiments of the present invention more clearly, institute is in need in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those skilled in the art, the other accompanying drawings obtained without creative efforts belong to this hair
Bright protection domain.
WiFi-PF algorithms are the particle filter algorithm based on WiFi and PDR in the present invention, and Mag-PF algorithms are based on earth magnetism
With the particle filter algorithm of PDR, the fingerprint matching algorithm based on WiFi uses the arest neighbors matching algorithm based on WiFi,
WiFi-KNN algorithms are the arest neighbors matching algorithm based on WiFi, and PDR algorithms are pedestrian's dead-reckoning algorithms, and KNN algorithms are most
Nearest neighbor algorithm.
Initial point coordinates is determined using initial position fix algorithm, is then filtered by merging the particle of PDR, WiFi and earth magnetism
Wave algorithm realizes that positioning, detailed process are as follows：
Using accurate initial position fix algorithm, determines initial point coordinates, specifically include following steps：
Step 1：Pedestrian starts the N steps after walking, is added to smart mobile phone by pedestrian's dead-reckoning algorithms (PDR algorithms)
The side that speed counts, gyro data, electronic compass data and magnetometer data estimate the step number of pedestrian's walking and often walk
To；
Step 2：Initial point region is determined using WiFi, within the N step times that pedestrian's starting row is walked, the WiFi of smart mobile phone
Scan module carries out periodical WiFi signal scanning, therefore can collect the signal strength vector of multiple WiFi, to multiple
The signal strength vector of WiFi carries out average value processing, obtains the WiFi average signal strength vectors in this time, using nearest
Adjacent algorithm (KNN algorithms) is positioned, and positioning result is obtainedThen it sets with positioning result initial point region to
For the center of circle, R0For the circle of radius；
Step 3：It is accurately positioned using earth magnetism three-dimensional series data, determines that initial point coordinates, principle are to utilize maximum
Posterior estimator criterion, by observation come the posterior probability of sample estimates, to estimate that initial point coordinates, detailed process are as follows：
1. in the initial point region that step 2 determines, generates and obey equally distributed NumI sample, sample position set
For { pi=(xi,yi), i=1,2 ..., NumI }；
2. each sample i is according to step number N, the step-length d estimated in step 1iAnd the mean direction in preceding N steps directionIt transports forward
Dynamic, movement locus is denoted as li, i-th of sample equation of motion be
Wherein, step-length diWith direction of motion θiIt is all added to Gaussian noise, d indicates every and grows step by step,Indicate N long Nd step by step
Variance,Indicate mean directionVariance；
3. for i-th of sample, its movement locus l is obtainediCorresponding earth magnetism three-dimensional vector sequence Mi, earth magnetism three-dimensional vector
Sequence MiIt is obtained by earth magnetism fingerprint linear interpolation in database；Observation is collected earth magnetism three-dimensional vector sequence in preceding N steps
Arrange Mnow, pass through MnowCalculate the posterior probability P of i-th of samplei, the posterior probability calculation of i-th of sample is as follows：Calculate the
The corresponding earth magnetism three-dimensional vector sequence M of i sampleiCollected earth magnetism sequence M in being walked with preceding NnowBetween DTW distances be denoted as Di,
Then the weight of i-th of sample isThen posterior probability is the value after weight normalizationWhereinσD 2It indicates
The variance of DTW distances；
4. selecting the maximum K sample of posterior probability, and carry out 3 σ criterion rejecting abnormalities samples, remaining K0A sample { pj=
(xj,yj), j=1,2 ..., K0, finally to K0The position coordinates of a sample carry out average value processing, calculate initial point coordinates
It is realized and is positioned by the particle filter algorithm of fusion boat position information, WiFi and earth magnetism, detailed process is as follows：
Step 1：With L0(x0,y0) it is that initial point carries out particle filter algorithm (the WiFi-PF calculations based on WiFi and PDR respectively
Method) and particle filter algorithm (Mag-PF algorithms) based on earth magnetism and PDR particle initialization, the detailed process of initialization be with
L0(x0,y0) it is the center of circle, R is that radius draws circle, Num particle of random distribution in circle, as primary collection, wherein needs pair
WiFi-PF algorithms and Mag-PF algorithms generate respective primary collection；
Step 2：Real-time estimation user walking step number and direction are carried out using PDR algorithms, PDR algorithms are detected
Kth walks, and can obtain the elements of a fix of corresponding WiFi-PF algorithmsThe positioning of Mag-PF algorithms is sat
MarkAnd the elements of a fix of WiFi-KNN
Step 3：Kth walks, by the elements of a fix for merging the particle filter algorithm (Mag-PF algorithms) based on earth magnetism and PDR
Carry out whether locating and tracking unsuccessfully judges that detailed process is as follows with cartographic information：
It detects in 20 steps before kth step, the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) enter unreachable region
Number RIM-PF, i.e. the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) enter behind unreachable region start again it is initial
The number RI of changeM-PF, compare the positioning result of Mag-PF algorithms into the number RI in unreachable regionM-PFWith thresholding TM-PFPass
System, if RIM-PF>TM-PF, then illustrate the locating and tracking failure of Mag-PF algorithms；Otherwise, illustrate the locating and tracking of Mag-PF algorithms just
Often；
Step 4：It is reference with the elements of a fix of WiFi-PF algorithms if judging the locating and tracking failure of Mag-PF algorithms
Particle is reinitialized, i.e., with the elements of a fix of the WiFi-PF algorithms of current kth stepFor the center of circle, R
It draws and justifies for radius, generate Num particle at random in circle, instead of primary particle collection, particle is carried out as next step Mag-PF algorithms
The particle collection of filtering, then carry out step 6；If judging, the locating and tracking of Mag-PF algorithms is normal, carries out step 5；
Step 5：When kth walks, by the elements of a fixWith the elements of a fix
It is merged：According to the elements of a fixWith the WiFi-KNN algorithm elements of a fixBetween
DistanceThe elements of a fixWith the elements of a fixBetween distance
By the elements of a fixAnd the elements of a fixIt is weighted to obtain the elements of a fixThe two weighting weight be respectivelyWherein,For the elements of a fixWeight,For the elements of a fixWeight；
Parameter ωkCalculation there are two types of：
1. counting backward technique：
2. index method：
So, the elements of a fix after WiFi-PF algorithms are merged with the elements of a fix of Mag-PF algorithms are
Calculation formula is
Step 6：Using the elements of a fix of WiFi-PF algorithms as the elements of a fix of fusion, i.e.,
Step 7：Calculate the elements of a fix of WiFi-KNNWith the particle filtering coordinate mergedThe distance betweenIf the elements of a fix of WiFi-KNN algorithmsWith the particle filtering coordinate mergedThe distance between diskMore than or equal to thresholding
δ then illustrates that WiFi-PF algorithms fail with Mag-PF algorithm locating and trackings, to the particle in WiFi-PF algorithms and Mag-PF algorithms
Collection is respectively processed, and is increased a part and is distributed in positionThe random particles of surrounding, more new particle collection, that
The elements of a fix that updated particle works in+1 step of kth, and currently walksIf
The elements of a fix of WiFi-KNN algorithmsWith the particle filtering coordinate mergedBetween
Distance diskLess than thresholding δ, thenAs the elements of a fix currently walked
Step 8：If detecting, meter step, k=k+1 jump to step 3；Otherwise, positioning terminates.
Wherein, start the detailed process reinitialized in step 3 in Mag-PF algorithms：Refer to when kth step Mag-PF is calculated
When the elements of a fix of method reach unreachable region, reinitialized according to history location information, the specific steps are：
1. particle initializes：For the elements of a fix before being walked using n as the center of circle, R is that radius draws circle, random in circle to generate Num
The step number walked in the same direction before particle, wherein kth step is p, then
2. state shifts：The direction of Particles Moving be n step in mean direction, step-length be n times often grow step by step；
3. observation：Pedestrian collected earth magnetism three-dimensional series B in n is walkednow；
4. right value update：Movement locus corresponding earth magnetism three-dimensional series B of i-th of particle in n stepsi, can be according to particle
The Origin And Destination coordinate of estimation carries out linear interpolation by earth magnetism fingerprint in database to be obtained；Calculate i-th particle
Earth magnetism three-dimensional series BiWith observation BnowBetween DTW distances be denoted as Di, then the weight of i-th of particle beThen right
Weight is normalized, wherein σD 2Indicate the variance of DTW distances；
5. resampling：Carry out simple randomization resampling；
6. location estimation：It is averaged to the position of all particles, obtains the elements of a fix of kth step.
With reference to the attached drawing of the present invention, technical scheme in the embodiment of the invention is clearly and completely described：
Test environment is certain opening office, and test environment size is 52m × 60m, and specific test environment plan view is such as
Shown in Fig. 3, experiment test mobile phone is Huawei's honor magic mobile phones.
Off-line phase, WiFi fingerprints use the static state at fixed anchor point to acquire 1min, and anchor point spacing is 3m, by each anchor point
Locate collected a plurality of WiFi signal intensity vector and carry out average value processing, then by anchor point coordinate and corresponding WiFi signal intensity
Mean vector is uploaded to database；For earth magnetism fingerprint by the way of continuous walking acquisition, track route needs non-overlapping covering
All range coverages generate the earth magnetism finger print data of anchor point interval 0.5m then to the processing of collected earth magnetism series of discreteization
Library, i.e., each anchor point correspond to a dimensionally magnetic vector.
On-line stage, pedestrian hold mobile phone walking in test environment, and travel distance is about 280m, and positioning flow is as follows：
Using accurate initial position fix algorithm, determines initial point coordinates, specifically include following steps：
As shown in Figure 1, carrying out the algorithm flow chart of initial point location for on-line stage, it is as follows, in Fig. 1：
Step 1：Pedestrian starts the N steps after walking, is added to smart mobile phone by pedestrian's dead-reckoning algorithms (PDR algorithms)
Speed counts, gyro data and magnetometer data estimate the step number that pedestrian walks and the direction often walked, in embodiment N
=5.
Step 2：Initial point region is determined using WiFi, within the N step times that pedestrian's starting row is walked, the WiFi of smart mobile phone
Scan module carries out periodical WiFi signal scanning, therefore can collect the signal strength vector of multiple WiFi, to multiple
The signal strength vector of WiFi carries out average value processing, obtains the WiFi average signal strength vectors in this time, using nearest
Adjacent algorithm (KNN) is positioned to obtain positioning resultThen it sets with positioning result initial point region toFor circle
The heart, R0For the circle of radius.
Step 3：It is accurately positioned using earth magnetism three-dimensional series data, principle is to utilize MAP estimation criterion, is led to
The posterior probability that observation carrys out sample estimates is crossed, to estimate that initial point coordinates, detailed process are as follows：
In the initial point localization region that step 2 determines, generates and obey equally distributed NumI sample, sample position collection
It is combined into { pi=(xi,yi), i=1,2 ..., NumI }；
1. each sample i is according to step number N, the step-length d estimated in step 1iAnd the mean direction in preceding N steps directionIt transports forward
Dynamic, movement locus is denoted as, and i-th of sample equation of motion is
Wherein, step-length diWith direction of motion θiIt is all added to Gaussian noise, d indicates every and grows step by step,Indicate N long Nd step by step
Variance,Indicate mean directionVariance；
For i-th of sample, its movement locus l is obtainediCorresponding earth magnetism three-dimensional vector sequence Mi, earth magnetism three-dimensional vector sequence
Arrange MiIt is obtained by earth magnetism fingerprint linear interpolation in database；
2. observation is collected earth magnetism three-dimensional vector sequence M in preceding N stepsnow, pass through MnowAfter calculating i-th of sample
Test probability Pi, the posterior probability calculation of i-th of sample is as follows：Calculate the corresponding earth magnetism three-dimensional vector sequence M of i-th of samplei
Collected earth magnetism sequence M in being walked with preceding NnowBetween DTW distances be denoted as Di, then the weight of i-th of sample beThen
Posterior probability is the value after weight normalizationWhereinσD 2Indicate the variance of DTW distances；
3. selecting the maximum K sample of posterior probability, and carry out 3 σ criterion rejecting abnormalities samples, remaining K0A sample { pj=
(xj,yj), j=1,2 ..., K0, finally to K0The position coordinates of a sample carry out average value processing, calculate initial point coordinates
It is realized and is positioned by the particle filter algorithm of fusion boat position information, WiFi and earth magnetism, is as follows：
As shown in Fig. 2, for the blending algorithm flow chart of on-line stage, in Fig. 2：
Step 1：With L0(x0,y0) it is that initial point carries out particle filter algorithm (the WiFi-PF calculations based on WiFi and PDR respectively
Method) and particle filter algorithm (Mag-PF algorithms) based on earth magnetism and PDR particle initialization, the detailed process of initialization be with
L0(x0,y0) it is the center of circle, R is that radius draws circle, Num particle of random distribution in circle, as primary collection, wherein needs pair
WiFi-PF algorithms and Mag-PF algorithms generate respective primary collection；
Step 2：Real-time estimation user walking step number and direction are carried out using PDR algorithms, PDR algorithms are detected
Kth walks, and can obtain the elements of a fix of corresponding WiFi-PF algorithmsThe positioning of Mag-PF algorithms is sat
MarkAnd the elements of a fix of WiFi-KNN
Step 3：Kth is walked, the elements of a fix estimated using Mag-PF algorithmsJudge it
It whether in unreachable region, is reinitialized if so, starting, and records the step number reinitialized.Using historical information into
Row reinitializes.Wherein, unreachable region is mesh free region in Fig. 3.The flow reinitialized is as follows：
1. particle initializes：For the elements of a fix before being walked using n as the center of circle, R is that radius draws circle, random in circle to generate Num
The step number walked in the same direction before particle, wherein kth step is p, thenWherein R is particle when positioning starts
The radius of initialization；
2. state shifts：The direction of Particles Moving be n step in mean direction, step-length be n times often grow step by step；
3. observation：Pedestrian collected earth magnetism three-dimensional series B of smart mobile phone in n is walkednow；
4. right value update：Movement locus corresponding earth magnetism three-dimensional series B of i-th of particle in n stepsi, earth magnetism three-dimensional sequence
Arrange BiCan carry out linear interpolation by earth magnetism fingerprint in database according to the Origin And Destination coordinate of Particles Moving track can obtain；Meter
Calculate the earth magnetism three-dimensional series B of i-th of particleiWith observation BnowBetween DTW distances be denoted as Di, then the weight of i-th of particle beThen weight is normalized, wherein σD 2Indicate the variance of DTW distances；
5. resampling：Simple randomization resampling is carried out according to particle collection position coordinates and particle weights；
6. location estimation：The elements of a fix of kth step are averagely obtained to the position of all particles.
Step 4：It is walked in kth, fusion is passed through to the elements of a fix of the particle filter algorithm (Mag-PF algorithms) based on earth magnetism
Cartographic information carries out Mag-PF algorithms positioning failure and self detects：
It detects in 20 steps before kth step, the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) enter unreachable region
Number, i.e. the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) enter unreachable region after start reinitialize time
Number RIM-PF, compare the positioning result of Mag-PF algorithms into the number RI in unreachable regionM-PFWith thresholding TM-PFRelationship, if
RIM-PF>TM-PF, then illustrate the locating and tracking failure of Mag-PF algorithms；Otherwise, illustrate that the locating and tracking of Mag-PF algorithms is normal；Its
Middle thresholding TM-PF=5.
Step 5：It is reference with the elements of a fix of WiFi-PF algorithms if judging the locating and tracking failure of Mag-PF algorithms
Particle is reinitialized, i.e., with the elements of a fix of the WiFi-PF algorithms of current kth stepFor the center of circle, R
It draws and justifies for radius, generate Num particle at random in circle, instead of primary particle collection, particle is carried out as next step Mag-PF algorithms
The particle collection of filtering, then carry out step 7；If judging, the locating and tracking of Mag-PF algorithms is normal, carries out step 6；
Step 6：Kth walks, and Mag-PF algorithms do not detect that Particle tracking fails, by the elements of a fixWith the elements of a fixIt is merged：According to the elements of a fixAnd the elements of a fixWith the WiFi-KNN algorithm elements of a fixBetween distanceWithBy the elements of a fixAnd the elements of a fixIt is weighted to obtain the elements of a fixThe two weighting weight be respectivelyWherein,For the elements of a fixWeight,For the elements of a fixWeight；Parameter ωkCalculation there are two types of：
1. counting backward technique：
2. index method：
Wherein, the elements of a fixWith the WiFi-KNN algorithm elements of a fixBetween
Distance calculation formula be
The elements of a fixWith the elements of a fixBetween distance calculation formula
For
So, the elements of a fix after WiFi-PF algorithms are merged with Mag-PF algorithm positioning results areMeter
Calculating formula is
If Mag-PF algorithms detect that Particle tracking fails, using the elements of a fix of WiFi-PF algorithms determining as fusion
Position coordinate, i.e.,
In the present embodiment, the weighting of WiFi-PF algorithms and the Mag-PF algorithm elements of a fix is carried out using index method.
Step 7：Calculate the elements of a fix of WiFi-KNN algorithmsIt is sat with the particle filtering merged
MarkThe distance betweenIf the positioning of WiFi-KNN algorithms is sat
MarkWith the particle filtering coordinate mergedThe distance between diskMore than or equal to door
δ is limited, then illustrates that WiFi-PF algorithms fail with the positioning of Mag-PF algorithms.Therefore it needs to be updated a subset op, to WiFi-
PF algorithms and the population of Mag-PF algorithms are respectively processed, and are increased a part and are distributed in coordinateAround
Random particles, more new particle collection, then updated particle works in+1 step of kth, and the elements of a fix currently walked are
The elements of a fix of WiFi-PF algorithms and Mag-PF algorithm weightsIf WiFi-KNN algorithms
The elements of a fixWith the particle filtering coordinate mergedThe distance between diskLess than door
δ is limited, then direct handleAs the elements of a fix currently walked
Step 8：If detecting, meter step, k=k+1 jump to step 3；Otherwise, positioning terminates.
The positioning result of the present embodiment is as shown in table 1, and table 1 is WiFi-KNN (wifi) algorithm, Mag-PF algorithms, WiFi-
The average localization error and Hybrid-PF of PF algorithms and fusion location algorithm (Hybrid-PF) are fixed compared with other three kinds of algorithms
The percentage of position precision improvement：
| Algorithm title | WiFi-KNN | Mag-PF | WiFi-PF | Hybrid-PF |
| Average localization error (m) | 2.86 | 2.79 | 3.54 | 2.41 |
| Positioning accuracy promotes (%) | 15.73 | 13.62 | 31.92 | - |
As it can be seen from table 1 the positioning method accuracy of the present invention is high.
Claims (9)
1. a kind of indoor orientation method based on particle filter algorithm, which is characterized in that include the following steps：Using initial position
Location algorithm determines initial point coordinates, then realizes positioning by merging the particle filter algorithm of PDR, WiFi and earth magnetism, specifically
Process is as follows：
Step 1：With L0(x0,y0) it is the particle initialization that initial point carries out WiFi-PF algorithms and Mag-PF algorithms respectively；
Step 2：Real-time estimation user walking step number and direction, the kth detected for PDR algorithms are carried out using PDR algorithms
Step, obtains the elements of a fix of corresponding WiFi-PF algorithmsThe elements of a fix of Mag-PF algorithmsAnd the elements of a fix of WiFi-KNN algorithms
Step 3：When kth walks, the elements of a fix by merging Mag-PF algorithms carry out whether locating and tracking fails with cartographic information
Judge；
Step 4：It is with reference to again with the elements of a fix of WiFi-PF algorithms if judging the locating and tracking failure of Mag-PF algorithms
Particle is initialized, i.e., with the elements of a fix of the WiFi-PF of current kth stepFor the center of circle, R draws for radius
Circle generates Num particle at random in circle, and instead of primary particle collection, the grain of particle filter is carried out as next step Mag-PF algorithms
Subset, then carry out step 6；If judging, the locating and tracking of Mag-PF algorithms is normal, carries out step 5；
Step 5：When kth walks, by the elements of a fixWith the elements of a fixIt carries out
Fusion, obtaining the elements of a fix after WiFi-PF algorithms are merged with the elements of a fix of Mag-PF algorithms is
Step 6：Using the elements of a fix of WiFi-PF algorithms as the elements of a fix after fusion, i.e.,
Step 7：Calculate the elements of a fix of WiFi-KNN algorithmsWith merge after the elements of a fixThe distance betweenIf the elements of a fix of WiFi-KNN algorithmsWith merge after the elements of a fixThe distance between diskMore than or equal to thresholding δ, then say
Bright WiFi-PF algorithms fail with Mag-PF algorithm locating and trackings, distinguish the particle collection in WiFi-PF algorithms and Mag-PF algorithms
It is handled, increases a part and be distributed in positionThe random particles of surrounding, more new particle collection, then updating
Particle afterwards works in+1 step of kth, the elements of a fix currently walkedIf WiFi-KNN is calculated
The elements of a fix of methodWith merge after the elements of a fixThe distance between diskLess than door
δ is limited, then willAs the elements of a fix currently walked
Step 8：If detecting, meter step, k=k+1 jump to step 3；Otherwise, positioning terminates.
2. a kind of indoor orientation method based on particle filter algorithm according to claim 1, which is characterized in that step 1
Middle initialization detailed process is：With L0(x0,y0) it is the center of circle, R is that radius picture is justified, Num particle of random distribution in circle, as
Primary collection, wherein respective primary collection is generated to WiFi-PF algorithms and Mag-PF algorithms.
3. a kind of indoor orientation method based on particle filter algorithm according to claim 1, which is characterized in that step 3
The detailed process whether middle progress locating and tracking unsuccessfully judges is as follows：
It detects in 20 steps before kth step, the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) enter the secondary of unreachable region
Number RIM-PF, i.e. the elements of a fix L of Mag-PF algorithmsM-PF(xM-PF,yM-PF) reinitialized into startup behind unreachable region
Number RIM-PF, compare the positioning result of Mag-PF into the number RI in unreachable regionM-PFWith thresholding TM-PFRelationship, if
RIM-PF>TM-PF, then illustrate the locating and tracking failure of Mag-PF algorithms；Otherwise, illustrate that the locating and tracking of Mag-PF algorithms is normal.
4. a kind of indoor orientation method based on particle filter algorithm according to claim 2, which is characterized in that start weight
The detailed process newly initialized is as follows：
1. particle initializes：For the elements of a fix before being walked using n as the center of circle, R is that radius draws circle, generates Num particle at random in circle,
The step number wherein walked in the same direction before kth step is p, then
2. state shifts：The direction of Particles Moving be n step in mean direction, step-length be n times often grow step by step；
3. observation：Pedestrian collected earth magnetism three-dimensional series B in n is walkednow；
4. right value update：Movement locus corresponding earth magnetism three-dimensional series B of i-th of particle in n stepsi, estimated according to Particles Moving
Origin And Destination coordinate by database earth magnetism fingerprint carry out linear interpolation can obtain；The earth magnetism for calculating i-th of particle is three-dimensional
Sequence BiWith observation BnowBetween DTW distances be denoted as Di, then the weight of i-th of particle beThen weight is carried out
Normalization, wherein σD 2Indicate the variance of DTW distances；
5. resampling：Carry out simple randomization resampling；
6. location estimation：It is averaged to the position of all particles, obtains the elements of a fix of kth step.
5. a kind of indoor orientation method based on particle filter algorithm according to claim 1, which is characterized in that step 5
Detailed process be：According to the elements of a fixWith the WiFi-KNN algorithm elements of a fixBetween distanceThe elements of a fixWith the elements of a fixBetween distanceBy the elements of a fixAnd the elements of a fixIt is weighted to obtain the elements of a fixThe two weighting weight be respectivelyWherein,For the elements of a fixWeight,For the elements of a fixWeight, ωkFor parameter.
6. a kind of indoor orientation method based on particle filter algorithm according to claim 5, which is characterized in that parameter ωk
Calculation there are two types of：
1. counting backward technique：
2. index method：
7. a kind of indoor orientation method based on particle filter algorithm according to claim 5, which is characterized in that WiFi-
The elements of a fix after PF algorithms are merged with the elements of a fix of Mag-PF algorithms areCalculation formula is
8. a kind of indoor orientation method based on particle filter algorithm according to claim 1, which is characterized in that using just
Beginning position location algorithms determine that the detailed process of initial point coordinates is as follows：
Step 1：Pedestrian starts the N steps after walking, accelerometer data, top by pedestrian's dead-reckoning algorithms to smart mobile phone
The direction that spiral shell instrument data, electronic compass data and magnetometer data estimate the step number of pedestrian's walking and often walk；
Step 2：Initial point region is determined using WiFi, and within the N step times that pedestrian's starting row is walked, the WiFi of smart mobile phone is scanned
Module carries out periodical WiFi signal scanning, therefore collects the signal strength vector of multiple WiFi, to the signal of multiple WiFi
Intensity vector carries out average value processing, obtains the WiFi average signal strength vectors in this time, is carried out using nearest neighbor algorithm
Positioning, obtains positioning resultThen it sets with positioning result initial point region toFor the center of circle, R0For the circle of radius；
Step 3：It is accurately positioned using earth magnetism three-dimensional series data, determines that initial point coordinates, principle are to utilize maximum a posteriori
Estimation criterion, by observation come the posterior probability of sample estimates, to estimate initial point coordinates.
9. a kind of indoor orientation method based on particle filter algorithm according to claim 8, which is characterized in that step 3
The detailed process of the middle initial point coordinates of estimation is as follows：
1. in the initial point region that step 2 determines, generate and obey equally distributed NumI sample, sample position collection is combined into
{pi=(xi,yi), i=1,2 ..., NumI }；
2. each sample i is according to step number N, the step-length d estimated in step 1iAnd the mean direction in preceding N steps directionIt travels forward, transports
Dynamic rail mark is denoted as li, i-th of sample equation of motion be
Wherein, step-length diWith direction of motion θiIt is all added to Gaussian noise, d indicates every and grows step by step,Indicate the side of N long Nd step by step
Difference,Indicate mean directionVariance；
For i-th of sample, its movement locus l is obtainediCorresponding earth magnetism three-dimensional vector sequence Mi, earth magnetism three-dimensional vector sequence Mi
It is obtained by earth magnetism fingerprint linear interpolation in database；
3. observation is collected earth magnetism three-dimensional vector sequence M in preceding N stepsnow, pass through MnowThe posteriority for calculating i-th of sample is general
Rate Pi, the posterior probability calculation of i-th of sample is as follows：Calculate the corresponding earth magnetism three-dimensional vector sequence M of i-th of sampleiWith it is preceding
Collected earth magnetism sequence M in N stepsnowBetween DTW distances be denoted as Di, then the weight of i-th of sample beThen posteriority
Probability is the value after weight normalizationWherein σD 2Indicate the variance of DTW distances；
4. selecting the maximum K sample of posterior probability, and carry out 3 σ criterion rejecting abnormalities samples, remaining K0A sample { pj=(xj,
yj), j=1,2 ..., K0, finally to K0The position coordinates of a sample carry out average value processing, calculate initial point coordinates
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810361226.2A CN108632761B (en) | 2018-04-20 | 2018-04-20 | Indoor positioning method based on particle filter algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810361226.2A CN108632761B (en) | 2018-04-20 | 2018-04-20 | Indoor positioning method based on particle filter algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108632761A true CN108632761A (en) | 2018-10-09 |
| CN108632761B CN108632761B (en) | 2020-03-17 |
Family
ID=63694173
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810361226.2A Expired - Fee Related CN108632761B (en) | 2018-04-20 | 2018-04-20 | Indoor positioning method based on particle filter algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108632761B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN109669201A (en) * | 2018-11-13 | 2019-04-23 | 斯沃德教育科技股份有限公司 | It is a kind of based on wearable indoor and outdoor location algorithm |
| CN109725329A (en) * | 2019-02-20 | 2019-05-07 | 苏州风图智能科技有限公司 | A kind of unmanned vehicle localization method and device |
| CN109883428A (en) * | 2019-03-27 | 2019-06-14 | 成都电科慧安科技有限公司 | A kind of high-precision locating method merging inertial navigation, earth magnetism and WiFi information |
| CN110260855A (en) * | 2019-06-24 | 2019-09-20 | 北京壹氢科技有限公司 | A kind of indoor pedestrian navigation localization method merging pedestrian's dead reckoning, Geomagnetism Information and indoor map information |
| CN110285809A (en) * | 2019-07-15 | 2019-09-27 | 李素敏 | A kind of integrated integrated positioning device of indoor and outdoor |
| CN110300385A (en) * | 2019-07-09 | 2019-10-01 | 桂林理工大学 | A kind of indoor orientation method based on adaptive particle filter |
| CN110307849A (en) * | 2019-07-15 | 2019-10-08 | 李素敏 | A kind of personnel's indoor positioning and indoor and outdoor integration localization method |
| CN110487273A (en) * | 2019-07-15 | 2019-11-22 | 电子科技大学 | A kind of indoor pedestrian track projectional technique of level meter auxiliary |
| CN110488818A (en) * | 2019-08-08 | 2019-11-22 | 深圳市银星智能科技股份有限公司 | A kind of robot localization method, apparatus and robot based on laser radar |
| CN110856255A (en) * | 2019-11-25 | 2020-02-28 | 北京眸星科技有限公司 | Anti-difference position fingerprint positioning method |
| CN111556432A (en) * | 2020-04-21 | 2020-08-18 | 西安交通大学 | Crowdsourcing fingerprint database construction method based on map information screening and matching |
| CN111623765A (en) * | 2020-05-18 | 2020-09-04 | 清华大学 | Indoor positioning method and system based on multi-mode data |
| CN113063420A (en) * | 2021-03-17 | 2021-07-02 | 建信金融科技有限责任公司 | Indoor positioning method and device, electronic equipment and storage medium |
| CN114710745A (en) * | 2022-04-12 | 2022-07-05 | 电子科技大学 | Indoor positioning method with Bluetooth and PDR information deeply fused |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103237348A (en) * | 2013-05-10 | 2013-08-07 | 重庆大学 | Wireless sensor network (WSN)-based improved particle filter moving object positioning method |
| CN103925923A (en) * | 2014-05-07 | 2014-07-16 | 南京大学 | Geomagnetic indoor positioning system based on self-adaptive particle filter algorithm |
| US20150018018A1 (en) * | 2013-07-12 | 2015-01-15 | Microsoft Corporation | Indoor Location-Finding using Magnetic Field Anomalies |
| CN105547298A (en) * | 2015-12-25 | 2016-05-04 | 北京京元智慧应急技术有限公司 | Indoor dynamic continuous positioning method fusing smartphone built-in sensor and Wi-Fi (Wireless Fidelity) |
| CN106289242A (en) * | 2016-07-18 | 2017-01-04 | 北京方位捷讯科技有限公司 | Particle filtering method and device based on earth magnetism |
| CN106610292A (en) * | 2015-10-22 | 2017-05-03 | 北京金坤科创技术有限公司 | Method of indoor positioning through combination of WIFI and pedestrian dead reckoning (PDR) |
| CN106767821A (en) * | 2016-12-09 | 2017-05-31 | 北京羲和科技有限公司 | A kind of map match localization method and system based on particle filter |
| CN107179079A (en) * | 2017-05-29 | 2017-09-19 | 桂林电子科技大学 | The indoor orientation method merged based on PDR with earth magnetism |
| CN107504968A (en) * | 2017-07-14 | 2017-12-22 | 临沂大学 | A kind of trajectory track method based on PDR and mobile target entry and exit point |
| CN107504971A (en) * | 2017-07-05 | 2017-12-22 | 桂林电子科技大学 | A kind of indoor orientation method and system based on PDR and earth magnetism |
-
2018
- 2018-04-20 CN CN201810361226.2A patent/CN108632761B/en not_active Expired - Fee Related
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103237348A (en) * | 2013-05-10 | 2013-08-07 | 重庆大学 | Wireless sensor network (WSN)-based improved particle filter moving object positioning method |
| US20150018018A1 (en) * | 2013-07-12 | 2015-01-15 | Microsoft Corporation | Indoor Location-Finding using Magnetic Field Anomalies |
| CN103925923A (en) * | 2014-05-07 | 2014-07-16 | 南京大学 | Geomagnetic indoor positioning system based on self-adaptive particle filter algorithm |
| CN106610292A (en) * | 2015-10-22 | 2017-05-03 | 北京金坤科创技术有限公司 | Method of indoor positioning through combination of WIFI and pedestrian dead reckoning (PDR) |
| CN105547298A (en) * | 2015-12-25 | 2016-05-04 | 北京京元智慧应急技术有限公司 | Indoor dynamic continuous positioning method fusing smartphone built-in sensor and Wi-Fi (Wireless Fidelity) |
| CN106289242A (en) * | 2016-07-18 | 2017-01-04 | 北京方位捷讯科技有限公司 | Particle filtering method and device based on earth magnetism |
| CN106767821A (en) * | 2016-12-09 | 2017-05-31 | 北京羲和科技有限公司 | A kind of map match localization method and system based on particle filter |
| CN107179079A (en) * | 2017-05-29 | 2017-09-19 | 桂林电子科技大学 | The indoor orientation method merged based on PDR with earth magnetism |
| CN107504971A (en) * | 2017-07-05 | 2017-12-22 | 桂林电子科技大学 | A kind of indoor orientation method and system based on PDR and earth magnetism |
| CN107504968A (en) * | 2017-07-14 | 2017-12-22 | 临沂大学 | A kind of trajectory track method based on PDR and mobile target entry and exit point |
Non-Patent Citations (1)
| Title |
|---|
| 周瑞，袁兴中等: "基于卡尔曼滤波的WiFi-PDR融合室内定位", 《电子科技大学学报》 * |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109413578B (en) * | 2018-11-02 | 2020-10-23 | 桂林电子科技大学 | Indoor positioning method based on fusion of WIFI and PDR |
| 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 |
| CN109669201A (en) * | 2018-11-13 | 2019-04-23 | 斯沃德教育科技股份有限公司 | It is a kind of based on wearable indoor and outdoor location algorithm |
| CN109725329A (en) * | 2019-02-20 | 2019-05-07 | 苏州风图智能科技有限公司 | A kind of unmanned vehicle localization method and device |
| CN109883428A (en) * | 2019-03-27 | 2019-06-14 | 成都电科慧安科技有限公司 | A kind of high-precision locating method merging inertial navigation, earth magnetism and WiFi information |
| CN109883428B (en) * | 2019-03-27 | 2022-11-01 | 成都电科慧安科技有限公司 | High-precision positioning method fusing inertial navigation, geomagnetic and WiFi information |
| CN110260855A (en) * | 2019-06-24 | 2019-09-20 | 北京壹氢科技有限公司 | A kind of indoor pedestrian navigation localization method merging pedestrian's dead reckoning, Geomagnetism Information and indoor map information |
| CN110260855B (en) * | 2019-06-24 | 2021-06-29 | 北京壹氢科技有限公司 | Indoor pedestrian navigation positioning method integrating pedestrian dead reckoning, geomagnetic information and indoor map information |
| CN110300385A (en) * | 2019-07-09 | 2019-10-01 | 桂林理工大学 | A kind of indoor orientation method based on adaptive particle filter |
| CN110285809A (en) * | 2019-07-15 | 2019-09-27 | 李素敏 | A kind of integrated integrated positioning device of indoor and outdoor |
| CN110487273A (en) * | 2019-07-15 | 2019-11-22 | 电子科技大学 | A kind of indoor pedestrian track projectional technique of level meter auxiliary |
| CN110307849A (en) * | 2019-07-15 | 2019-10-08 | 李素敏 | A kind of personnel's indoor positioning and indoor and outdoor integration localization method |
| CN110488818B (en) * | 2019-08-08 | 2020-07-17 | 深圳市银星智能科技股份有限公司 | Laser radar-based robot positioning method and device and robot |
| CN110488818A (en) * | 2019-08-08 | 2019-11-22 | 深圳市银星智能科技股份有限公司 | A kind of robot localization method, apparatus and robot based on laser radar |
| CN110856255A (en) * | 2019-11-25 | 2020-02-28 | 北京眸星科技有限公司 | Anti-difference position fingerprint positioning method |
| CN111556432A (en) * | 2020-04-21 | 2020-08-18 | 西安交通大学 | Crowdsourcing fingerprint database construction method based on map information screening and matching |
| CN111556432B (en) * | 2020-04-21 | 2022-02-01 | 西安交通大学 | Crowdsourcing fingerprint database construction method based on map information screening and matching |
| CN111623765A (en) * | 2020-05-18 | 2020-09-04 | 清华大学 | Indoor positioning method and system based on multi-mode data |
| CN113063420A (en) * | 2021-03-17 | 2021-07-02 | 建信金融科技有限责任公司 | Indoor positioning method and device, electronic equipment and storage medium |
| CN113063420B (en) * | 2021-03-17 | 2024-02-02 | 建信金融科技有限责任公司 | Indoor positioning method, device, electronic equipment and storage medium |
| CN114710745A (en) * | 2022-04-12 | 2022-07-05 | 电子科技大学 | Indoor positioning method with Bluetooth and PDR information deeply fused |
| CN114710745B (en) * | 2022-04-12 | 2023-04-18 | 电子科技大学 | Indoor positioning method with Bluetooth and PDR information deeply fused |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108632761B (en) | 2020-03-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108632761A (en) | A kind of indoor orientation method based on particle filter algorithm | |
| CN104796866B (en) | Indoor orientation method and device | |
| CN105588566B (en) | A kind of indoor locating system merged based on bluetooth with MEMS and method | |
| CN109298389A (en) | Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method | |
| Kim et al. | Indoor positioning system using geomagnetic anomalies for smartphones | |
| CN105424030B (en) | Fusion navigation device and method based on wireless fingerprint and MEMS sensor | |
| Kothari et al. | Robust indoor localization on a commercial smart phone | |
| Zhao et al. | Crowdsourcing and multisource fusion-based fingerprint sensing in smartphone localization | |
| CN103379619B (en) | A kind of localization method and system | |
| CN110455291B (en) | Method and apparatus for indoor location-finding using magnetic field anomalies | |
| CN106412839B (en) | Based on secondary partition and the matched indoor positioning of fingerprint gradient and tracking | |
| CN105043380A (en) | Indoor navigation method based on a micro electro mechanical system, WiFi (Wireless Fidelity) positioning and magnetic field matching | |
| Wang et al. | An indoor self-localization algorithm using the calibration of the online magnetic fingerprints and indoor landmarks | |
| CN106093843B (en) | A kind of smart phone user indoor orientation method based on earth magnetism auxiliary WiFi | |
| Zhang et al. | A localization database establishment method based on crowdsourcing inertial sensor data and quality assessment criteria | |
| CN108844533A (en) | A kind of free posture PDR localization method based on Multi-sensor Fusion and attitude algorithm | |
| CN108668245A (en) | A kind of indoor orientation method based on WiFi Yu earth's magnetic field database | |
| CN108521627B (en) | Indoor positioning system and method based on WIFI and geomagnetic fusion of HMM | |
| CN106767828A (en) | A kind of mobile phone indoor positioning solution | |
| CN106597363A (en) | Pedestrian location method in indoor WLAN environment | |
| US9651388B1 (en) | System and method for improved simultaneous localization and mapping | |
| US10557711B2 (en) | Apparatus for inferring pedestrian position based on pedestrian movement detection, and method therefor | |
| Lee et al. | An experimental heuristic approach to multi-pose pedestrian dead reckoning without using magnetometers for indoor localization | |
| CN107179079A (en) | The indoor orientation method merged based on PDR with earth magnetism | |
| CN108534779A (en) | One kind is corrected based on track and the improved indoor positioning map constructing method of fingerprint |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200317 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |