CN105910601B - A kind of indoor ground magnetic positioning method based on Hidden Markov Model - Google Patents
A kind of indoor ground magnetic positioning method based on Hidden Markov Model Download PDFInfo
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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
- G01C21/04—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means
- G01C21/08—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by terrestrial means involving use of the magnetic field of the earth
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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
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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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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Abstract
The indoor ground magnetic positioning method that the present invention relates to a kind of based on Hidden Markov Model, including off-line phase and on-line stage, off-line phase include: that area to be targeted is divided into grid according to the map；Geomagnetic field intensity data are measured in each grid element center RP using the magnetometer built in smart phone；Offline fingerprint base is constructed, offline fingerprint base is made of N number of fingerprint, and each finger print data includes fingerprint positions lw=[xw,yx] and fingerprint vector ξw=[μw,σw], tuning on-line includes: to determine paces length D according to step-size estimation and magnetometeriWith movement direction angle Φi, predict pedestrian position；Calculate state transition probability；Estimate the position after pedestrian's walking i step.The present invention only passes through smart phone i.e. and can reach higher indoor position accuracy.
Description
Technical field
The invention belongs to carry out the field of pedestrian's indoor positioning using Geomagnetism Information, especially under indoor complex environment
Orientation problem.
Background technique
In high precision and blanket indoor positioning has become more and more important in every field.For this purpose, many sections
Many location technologies have been proposed in the worker of grinding, such as based on arrival time (TOA), based on angle of arrival (AOA), based on arrival
Phase difference (PDOA), based on receiving signal energy (RSS), inertial navigation and fusion of front several method etc..In addition, with
It is also more and more in the signal type of indoor positioning, such as WiFi, UWB, Zigbee, these methods have been achieved for extraordinary
Positioning result.However, existing localization method largely requires additional hardware supported, and since wireless signal can be by people
Body absorbs, and wireless signal can be very faint or even receive and lead to positioning system in practical applications when the crowd is dense
Locating effect is bad.
Nowadays, sight has been turned to earth's magnetic field by Many researchers.Intrinsic resource of the earth's magnetic field as the earth is a vector
, with round-the-clock, round-the-clock and full region feature.Earth's magnetic field is not influenced and the distribution in indoor earth's magnetic field by human body
Mainly determined by fabric structure, and earth's magnetic field is highly stable after fabric structure determines, therefore earth's magnetic field is potential answers
For high-precision and blanket indoor positioning.Due to being influenced by indoor complex environment, especially armored concrete
It influences, indoor earth's magnetic field is complicated and changeable, and the GEOMAGNETIC FIELD of this variation can be used as a kind of finger corresponding with position just
Line information carries out matching positioning.In fact, earth's magnetic field has been widely used in indoor positioning.A kind of method is in inertial navigation system
The direction of motion is distinguished using magnetic field in system.Another method is to be carried out using geomagnetic field intensity as a kind of fingerprint using fingerprint technique
Positioning.Some researchers are with the help of inertial navigation system, using dynamic time programming (DTW) algorithm to the ground at continuous moment
Magnetic field strength information sequence carries out matching positioning.This method can achieve very high positioning accuracy, but this method is only fitted
The region long and narrow in this way for corridor is positioned.There are also some researchers using particle filter absolute force information and inertia
Navigation is merged, and this method needs largely to calculate to reach higher positioning accuracy during actual location.
It is problematic not handle fine, earth magnetism first although earth's magnetic field has been widely used in indoor positioning
Field intensity is very faint (only about tens uT), secondly for fingerprint technique, using single geomagnetic field intensity as fingerprint
It is too low come the resolution ratio of distinguishing different location.Although three axle magnetometer can obtain three-dimensional earth magnetism field data, naturally think
The resolution ratio of earth magnetism fingerprint, but in fact, three numbers that magnetometer acquires are improved to the geomagnetic field intensity using all three axis
It is therefore, utilizable indoors there was only total magnetic field strength according to that can change with the variation of sensor coordinate system.
Summary of the invention
For currently based in the indoor positioning technologies of earth's magnetic field earth's magnetic field weak output signal and resolution ratio is lower is difficult to answer
The problem of for fingerprint technique positioning, the present invention provides a kind of resolution ratio for increasing earth magnetism finger print information, obtains preferable positioning accurate
The indoor ground magnetic positioning method of degree.Technical scheme is as follows:
A kind of indoor ground magnetic positioning method based on Hidden Markov Model, including off-line phase and on-line stage,
The off-line data collecting stage the following steps are included:
1) area to be targeted is divided into grid, B according to the mapwFor w-th of grid；
2) geomagnetic field intensity data are measured in each grid element center RP using the magnetometer built in smart phone；
3) offline fingerprint base is constructed, offline fingerprint base is made of N number of fingerprint, and each finger print data includes fingerprint positions lw=
[xw,yx] and fingerprint vector ξw=[μw,σw], wherein μwAnd σwThe earth magnetism field data respectively acquired in w-th of grid is put down
Mean value and variance, L indicate the set of all grid element center RP compositions, L={ lw|1≤w≤N}；
The tuning on-line stage the following steps are included:
1) result for enabling the last time position isPositionThe position after people's walking i step is indicated, when detecting people's walking
One step determines paces length D according to step-size estimation and magnetometeriWith movement direction angle Φi, it is believed that DiWith ΦiIndependently of each other simultaneously
Gaussian distributed calculates separately DiWith ΦiProbability distribution；Using bayesian criterion, the probability distribution of pedestrian position is predictedSeek the probability distribution of pedestrian positionGreater than pTSet H:
Wherein pTThreshold probability l for setting is the position that personnel may currently exist；
2) calculate state transition probability: being located at the absolute force value sequence stored after pedestrian's walking i step is Oi:Wherein oi-k+1For the Geomagnetism Information measured when the i-th-k+1 step；
A. the intersection for calculating H and L is H', wherein li,jIndicate pedestrian position that may be present after walking i step, it may
Existing total number of positions is NP:
B. according to motion information for each position li,j=(xi,j,yi,j) N before predictionsA position, abscissa are
xi,j, ordinate yi,j, NsFor the length of sequence, li,j,k=(xi,j,k,yi,j,k) indicate the l predicted by PDRi,jBefore
K-th of position:
C. in the fingerprint positions l of offline fingerprint basewMiddle searching distance li,j,kNearest point, and stored in the grid respectively
The mean value and variance of heart RP is μi,j,kAnd σi,j,k；
D. for each li,jConstruct two backward sequences:With
E. assert that geomagnetic field intensity observation meets the Gaussian Profile centered on true value, calculates in position li,j,kOccur
Geomagnetic field intensity oi-k+1Probability:
F. for each possible position li,j, calculating observation value probability bi,j
G. to each position l that may be presenti,jCalculate state transition probability ai,j；
3) by ai,jAs the position after weight estimation pedestrian's walking i step.
The present invention is a kind of backward sequences match localization method based on Hidden Markov Model, and this method is walked using pedestrian
Detection is cut down to obtain motion information (PDR), increases the resolution ratio of earth magnetism finger print information after utilization to sequences match location technology,
Based on being positioned on the basis of Hidden Markov Model to pedestrian.And have very well for different user'ss (height, weight)
Robustness, it is hereby achieved that preferable positioning accuracy and calculation amount itself is less.Localization method of the invention exists
The emulation experiment for using monte carlo method to carry out more than 2000 times in MatLab.Test scene is 20 × 20 × 5 meters in emulation
The ranged space in, pedestrian's walking direction is random, sets the paces length and walking direction of pedestrian's walking, true to obtain
Position, positioning stage obtain compensation and movement direction angle in add Gaussian noise, meanwhile, in order to test environmental factor pair
The influence of locating effect considers the noise jamming of 0.1uT to 1uT in receiving geomagnetic field intensity signal.Simulation result shows
Under conditions of different noises, average positioning accuracy is 1.2 meters or so.The present invention is also adopted in smart phone (end Meizu MX3)
Collect data and carry out actual experiment, experimental site is located at 5 buildings, the Building D of 26 building, University Of Tianjin, is taken respectively by the volunteer of 5 different heights
Same portion's mobile phone carry out data acquisition, and positioned on computers.The experimental results showed that coming for people of different heights
It says, positioning accuracy has reached 1.4 meters or less.It is indicated above that not only positioning accuracy is higher by the present invention, but also there is good Shandong
Stick.
Detailed description of the invention
Fig. 1 is flow diagram of the invention, wherein (a) (b) (c) respectively represents position prediction stage, sequences match rank
Section and location estimation stage.
Specific embodiment
In order to be more clear technical solution of the present invention, below in conjunction with attached drawing and example, the present invention is carried out further
Detailed description.It should be appreciated that specific example described herein is only used for explaining invention, it is not intended to limit the present invention.Such as
The present invention shown in Fig. 1 includes three key steps: position prediction, backward sequences match and location estimation.
The off-line data collecting stage the following steps are included:
1) area to be targeted is divided into grid, B according to the mapwFor w-th of grid, grid element center (RP) is lw。
2) earth magnetism is measured in each grid element center using magnetic field measuring device (magnetometer, mobile phone with magnetic field sensor etc.)
Field intensity data.
3) offline fingerprint base is constructed, fingerprint base is made of N number of fingerprint.Each finger print data includes fingerprint positions lw=[xw,
yx] and fingerprint vector ξw=[μw,σw], wherein μwAnd σwThe average value of the earth magnetism field data respectively acquired in w-th of grid
And variance.L indicates the set of all RP compositions: L={ lw|1≤w≤N}。
The tuning on-line stage the following steps are included:
3) result for enabling the last time position isPositionIndicate the position after people's walking i step.When paces detection machine
System detects one step of people's walking, determines paces length D according to step-size estimation and magnetometeriWith movement direction angle Φi。
A) assume DiWith ΦiMutually indepedent and Gaussian distributed, calculates separately probability:
Wherein,WithRespectively DiWith ΦiProbability distribution, σdAnd σΦRespectively movement away from
The variance of walk-off angle degree, liFor this positioning position,For the estimated location of last time positioning.
B) bayesian criterion is utilized, predicts the probability distribution of pedestrian position
C) probability is soughtGreater than pTSet H:
Wherein pTThreshold probability l for setting is the position that personnel may currently exist.
4) it is matched using the geomagnetic field intensity information of inertial navigation information and online acquisition, calculates state transition probability.?
The absolute force value sequence stored after pedestrian's walking i step is Oi:Wherein oi-k+1For the i-th-k
The Geomagnetism Information measured when+1 step.
A) intersection for calculating H and L is H', wherein li,jIndicate pedestrian position that may be present after walking i step, it may
Existing total number of positions is NP:
B) according to motion information ∑ Δ liFor each position li,j=(xi,j,yi,j) N before predictionsA position (horizontal seat
It is designated as xi,j, ordinate yi,j), NsFor the length of sequence, in other words, li,j,k=(xi,j,k,yi,j,k) indicate by PDR come
The l of predictioni,jK-th of position (abscissa x beforei,j,k, ordinate yi,j,k):
C) in fingerprint base lwMiddle searching distance li,j,kNearest point, and mean value and the variance for storing the RP respectively are μi,j,k
And σi,j,k:
Wherein pwFor the position of RP.
D) for each li,jConstruct two backward sequences:With
E) assume that geomagnetic field intensity observation meets the Gaussian Profile centered on true value, calculate in position li,j,kOccur
Geomagnetic field intensity oi-k+1Probability:
Wherein μi,j,kThe σ for mean valuei,j,kFor corresponding variance, li-k+1For the position of personnel after the i-th-k+1 paces.
F) for each possible position li,j, compare Ui,jAnd Oi, calculating observation value probability bi,j(i.e. p (oi|li=
li,j)):
G) to each position l that may be presenti,jCalculate state transition probability ai,j, C is normaliztion constant:
5) by ai,jAs the position after weight estimation pedestrian's walking i step are as follows:
6) when system detects one step of pedestrian movement again, (3) step is arrived in repetition (1), estimates user location.
The present embodiment mainly includes that off-line data collecting and On-line matching position two stages.
Off-line phase mainly includes the following steps that, area to be targeted is divided into the grid of 0.6m × 0.6m first, with every
A net center of a lattice is as reference mode RP.Then, the instrument that can measure and record geomagnetic field intensity sensor, such as intelligence are carried
Energy mobile phone, IMU etc., according to sensor rate difference at about 100 groups of absolute force data of record of each grid element center (RP) measurement.
It averages to data and calculates variance.Finally, the position of RP and obtained average value and variance are referred to should be used as one
Line data construct fingerprint base.
In on-line stage, by taking smart phone as an example, user to be positioned with hand, walk in tow by mobile phone, and screen is placed upward, i.e.,
The direction of Y-axis orientation movements, such people in the process of walking, can be according to the acceleration of mobile phone upward for the Z axis of mobile phone
Sensor obtains motion information.The detection of paces can be carried out according to Z axis acceleration information, degree first can be accelerated to Z axis
According to being integrated, then using peak detection it is determined that paces.Due to the interference of noise etc., it is possible to be transported in a paces
Paces twice are detected in dynamic, for this purpose, a threshold time T can be setMINNo matter several paces are detected within this time,
All determine that the first step is a paces, according to experimental result, it is proposed that TMINIt is set as 0.3 second.It can be with according to Y-axis acceleration information
Estimate paces length.The direction of movement is obtained using magnetometer and acceleration transducer, also can use what gyroscope obtained
Angular velocity information obtains the direction of motion.
According to emulation and experimental result, NsIt is too small to will affect positioning accuracy, NsIt crosses conference and generates excessive calculation amount, therefore
It is recommended that NsIt is set as between 7 to 15.And PTSetting will lead to greatly very much system and be equivalent to inertial navigation, too small, will lead to matching model
It encloses for full map, it is therefore proposed that PTIt is set as between 0.4 to 0.65.
In this way, we have carried out the positioning experiment between multiple groups different people to the present invention.We have invited 5
The volunteer of different heights tests, and experimental result shows that mean accuracy can reach 1.2 meters or so.
Claims (1)
1. a kind of indoor ground magnetic positioning method based on Hidden Markov Model, including off-line phase and on-line stage,
The off-line data collecting stage the following steps are included:
1) area to be targeted is divided into grid, B according to the mapwFor w-th of grid；
2) geomagnetic field intensity data are measured in each grid element center RP using the magnetometer built in smart phone；
3) offline fingerprint base is constructed, offline fingerprint base is made of N number of fingerprint, and each finger print data includes fingerprint positions lw=[xw,
yx] and fingerprint vector ξw=[μw,σw], wherein μwAnd σwThe average value of the earth magnetism field data respectively acquired in w-th of grid
And variance,Indicate the set of all grid element center RP compositions,
The tuning on-line stage the following steps are included:
1) result for enabling the last time position isPositionIndicate the position after people's walking i step, when detecting one step of people's walking,
Paces length D is determined according to step-size estimation and magnetometeriWith movement direction angle Φi, it is believed that DiWith ΦiIndependently of each other and obey
Gaussian Profile calculates separately DiWith ΦiProbability distribution；Using bayesian criterion, the probability distribution of pedestrian position is predictedSeek the probability distribution of pedestrian positionGreater than pTSet
Wherein pTFor the threshold probability of setting, l is the position that personnel may currently exist；
2) calculate state transition probability: being located at the absolute force value sequence stored after pedestrian's walking i step is Oi:Wherein oi-k+1For the Geomagnetism Information measured when the i-th-k+1 step；
A. it calculatesWithIntersection beWherein li,jIndicate pedestrian position that may be present, Ke Nengcun after walking i step
Total number of positions be NP:
B. according to motion information for each position li,j=(xi,j,yi,j) N before predictionsA position, abscissa xi,j, indulge
Coordinate is yi,j, NsFor the length of sequence, li,j,k=(xi,j,k,yi,j,k) indicate the l predicted by PDRi,jBefore k-th
Position:
C. in the fingerprint positions l of offline fingerprint basewMiddle searching distance li,j,kNearest point, and grid element center RP is stored respectively
Mean value and variance be μi,j,kAnd σi,j,k；
D. for each li,jConstruct two backward sequences:With
E. assert that geomagnetic field intensity observation meets the Gaussian Profile centered on true value, calculates in position li,j,kThere is earth magnetism
Field intensity oi-k+1Probability:
F. for each possible position li,j, calculating observation value probability bi,j
G. to each position l that may be presenti,jCalculate state transition probability ai,j；
3) by ai,jAs the position after weight estimation pedestrian's walking i step.
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| CN106441302B (en) * | 2016-09-23 | 2020-06-12 | 上海交通大学 | Indoor positioning method in large-scale open area |
| CN106919785B (en) * | 2017-01-23 | 2019-07-16 | 哈尔滨工程大学 | It is a kind of that online compensation method in magnetic field is interfered based on the carrier of ground magnetic vector and particle filter |
| CN106682239B (en) * | 2017-02-06 | 2019-11-08 | 北京邮电大学 | One kind being based on the associated indoor location prediction technique of motion feature, apparatus and system |
| CN107635204B (en) * | 2017-09-27 | 2020-07-28 | 深圳大学 | Indoor fusion positioning method and device assisted by exercise behaviors and storage medium |
| CN108225324B (en) * | 2017-12-27 | 2021-08-17 | 中国矿业大学 | Indoor positioning method based on intelligent terminal and integrating geomagnetic matching and PDR |
| CN108521627B (en) * | 2018-03-14 | 2021-01-19 | 华南理工大学 | Indoor positioning system and method based on WIFI and geomagnetic fusion of HMM |
| CN109781094A (en) * | 2018-12-24 | 2019-05-21 | 上海交通大学 | Earth magnetism positioning system based on Recognition with Recurrent Neural Network |
| CN109743680B (en) * | 2019-02-28 | 2020-01-31 | 电子科技大学 | indoor on-line positioning method based on PDR combined with hidden Markov model |
| CN110057355B (en) * | 2019-04-18 | 2021-06-08 | 吉林大学 | Indoor positioning method, device and system and computing equipment |
| CN110287821B (en) * | 2019-06-06 | 2023-07-25 | 深圳数位大数据科技有限公司 | Fingerprint library collection method and device based on geomagnetic field |
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