CN109211229A - A kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature - Google Patents
A kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature Download PDFInfo
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- CN109211229A CN109211229A CN201810977144.0A CN201810977144A CN109211229A CN 109211229 A CN109211229 A CN 109211229A CN 201810977144 A CN201810977144 A CN 201810977144A CN 109211229 A CN109211229 A CN 109211229A
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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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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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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
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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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
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- 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
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Automation & Control Theory (AREA)
- Navigation (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature, the method is the initial position that pedestrian is determined by WiFi feature around, using the cadence of PDR algorithm resolving pedestrian, step-length, course and obtain the location information of each step of pedestrian, it is counted and calculated elevation information in conjunction with mobile phone acquisition air pressure, obtain the preliminary three dimensional local information of pedestrian, WiFi feature is acquired finally by mobile phone and preliminary three dimensional local information is corrected using the mathematical expression provided, obtains the final three dimensional local information of pedestrian.The present invention can be achieved with the continuous three-dimensional indoor positioning of high-precision using mobile phone, realize higher positioning result with lower cost.
Description
Technical field
The present invention relates to indoor positioning technologies fields, and in particular to a kind of personnel based on mobile phone sensor and WiFi feature
Indoor orientation method.
Background technique
Global Satellite Navigation System (GNSS) can be the head of outdoor positioning in the positioning service of outdoor offer real-time high-precision
It selects, however in building or place that the satellite-signals such as area that high building is densely covered are blocked, there are GNSS positioning accuracies
Poor or even not available situation.Because user has 70% to 90% time to spend indoors.There are many available rooms at present
Interior location technology, including WIFI, ultrasonic wave, low-power consumption bluetooth (BLE), radio frequency identification (RFID), purple honeybee (ZigBee), ultra wide band
Technology (UWB), magnetic signature, the vision guided navigation based on camera, is based on motion sensor (accelerometer, top at intelligent LED lamp
Spiral shell instrument, magnetometer) pedestrian's reckoning (PDR) etc..For any airmanship, all there is performance and cost
Compromise, higher positioning accuracy are usually associated with higher use cost, such as super-broadband tech (UWB), positioning accuracy energy
Reach Centimeter Level even grade, but it needs to arrange specific beaconing nodes, expend a large amount of cost of layout and calculate cost,
It is not suitable for consumer level positioning and extensive positioning.Comprehensively consider positioning performance and positioning cost, as far as possible raising performance and drop
Low cost is the developing direction of consumer level indoor positioning technologies.
Summary of the invention
The present invention provides a kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature, uses mobile phone energy
It realizes the continuous three-dimensional indoor positioning of high-precision, realizes higher positioning result with lower cost.Although WiFi is positioned
WiFi node is needed, but WiFi has been popularized at present, can be obtained in public daily life, often not needed additional cloth
It sets.
The present invention determines the initial position of pedestrian by surrounding WiFi feature, and cadence, the step of pedestrian are resolved using PDR algorithm
Long, course simultaneously obtains the location information of each step of pedestrian, counts and calculated elevation information, obtains in conjunction with mobile phone acquisition air pressure
To the preliminary three dimensional local information of pedestrian, preliminary three dimensional local information is corrected finally by mobile phone acquisition WiFi feature,
Obtain the final three dimensional local information of pedestrian.The final three dimensional local information is calculated by following mathematical expressions and is obtained:
Wherein: UkIndicate final three dimensional local information；xk、yk、zkRespectively indicate pedestrian's kth step three that WiFi is positioned
Tie up coordinate position；xk-1、yk-1Respectively indicate -1 step two-dimensional coordinate position of pedestrian's kth that WiFi is positioned；ψkIndicate that pedestrian exists
The course angle of kth step, VkFor the systematic observation noise of 6 dimensions.
It can use common PDR algorithm to resolve pedestrian movement's state of pedestrian, cadence, step-length, course and obtain pedestrian
The location information of each step.For pedestrian movement's state judgement in PDR algorithm, the present invention provides a preferred embodiment: first
Definition: a indicates three axis total accelerations, and σ, u respectively indicate total acceleration sequence { a1,a2,…,aNStandard deviation and mean value, σ (i)
The dead-center position of the i-th step is respectively indicated to the total acceleration sequence { a of i+1 step dead-center position with u (i)1,a2,…,aNMark
Quasi- difference and mean value, σ (i-1) and u (i-1) respectively indicate the dead-center position of the (i-1)-th step to the total acceleration sequence of the i-th step dead-center position
Arrange { a1,a2,…,aNStandard deviation and mean value, a (i, k) indicate the i-th step in k-th of total acceleration value, a (i-1, k) indicate
K-th of total acceleration value in (i-1)-th step；Then the correlation coefficient ρ of three axis total accelerations is calculated, calculation formula isWhen related coefficient is greater than 0.7, judge that personnel are in fortune
Dynamic state.
For pedestrian's step-length estimation in PDR algorithm, the present invention also provides a preferred embodiment: using non-linear step-length mould
Type is estimated to obtain, and model formation isWherein, dkIndicate step-length when kth step, AmaxAnd AminRespectively
Indicate three axis total acceleration maximum values and the minimum value in a step, λ is model coefficient, by least square in training before positioning
It obtains.
Detailed description of the invention
Fig. 1 is functional block diagram of the invention.
Specific embodiment
The present invention utilizes the real time position of mobile phone locating personnel, needs to use three axis accelerometer built in mobile phone, three axis tops
Spiral shell instrument, three axle magnetometer, barometer and WiFi sensor, indoor positioning mechanism consist of three parts, and are that pedestrian's track pushes away respectively
Calculate (PDR), WiFi positioning and the fixed height of floor.Pedestrian's reckoning is made of inertial sensor and magnetometer, its advantage is that in short-term
Positioning accuracy is high, but error can be accumulative with travel distance.WiFi positioning belongs to absolute fix, and position error at any time and is not moved
It moves distance and changes, but positioning accuracy is influenced by WiFi number of nodes and received signal strength (RSSI), usual positioning accuracy exists
3m is between 10m.Both fusions positioning result, the positioning result of pedestrian's reckoning is corrected using WiFi positioning result, can
The cumulative errors for effectively avoiding pedestrian's reckoning, obtain the high accuracy positioning of long-time stable as a result, position error is controlled
Within 1m.Finally, adding personnel's elevation information, indoor occupant real-time three-dimensional location information can be obtained.One, pedestrian's track pushes away
It calculates (PDR):
Pedestrian's reckoning (PDR) is partially made of three axis accelerometer, three-axis gyroscope and three axle magnetometer, is belonged to used
Property navigation field, traditional inertial navigation be by measure carrier acceleration, obtain relative position by quadratic integral, then adopt
Error is modified with zero-velocity curve method (ZUPT).But the inertial sensor precision as built in mobile phone is lower and pedestrian walks
The unstability of gait can not accurately detect zero-speed section, this meeting so that position error with the time square increase, even if mesh
Mark does not move, and error can also accumulate always.Different with traditional inertial navigation, pedestrian's reckoning (PDR) is according to pedestrian
Gait feature carries out location estimation, it passes through detection cadence and judges whether pedestrian determines in movement compared to traditional inertial navigation
Position error is only related with the travel distance of pedestrian, is unrelated with the time.
Pedestrian's reckoning includes three key problems: cadence detection, step-size estimation and course estimation.Its positioning principle is just
It is accelerometer, gyroscope and the magnetometer information built in by acquisition mobile phone, obtains the cadence, step-length and course of pedestrian, then
Go out the position at current time according to the dead reckoning of previous moment pedestrian.Wherein cadence detection and step-size estimation are by accelerometer
Complete, personnel when walking acceleration information have cyclically-varying, first according to acceleration information judge the static of personnel or
Walking states, personnel have following several modes: hand-held mode, pocket pattern and mode of making a phone call when walking using mobile phone again,
Cadence detection algorithm first determines whether out which kind of walking mode pedestrian is in using autocorrelation model, then according to the week of walking mode
The cadence of phase property detection pedestrian utilizes acceleration peak in a cycle finally according to the variation of acceleration information in a cycle
Peak-data non-linear estimations pedestrian's step-length.Course estimation is made of accelerometer, gyroscope and magnetometer sensor, gyroscope
High-precision course information in short-term can be provided, but course error can add up at any time, and accelerometer and magnetometer can be with
Absolute course information is provided, but earth magnetic field information is easy the interference by extraneous earth magnetism, melts using Extended Kalman filter
Close each sensing data, so that it may obtain the course information of long-time stable.Known users initial position and cadence step-length, course
Information can then extrapolate the position of each step after user.
Two .WiFi positioning:
WiFi positioning is realized by WiFi sensor, including two parts: the foundation of WiFi fingerprint map and online
WiFi fingerprint matching.The foundation of WiFi fingerprint map is to determine several samplings according to certain spacing distance in area to be targeted
Point forms a sampled point set.Two parts data are recorded in each sampled point, a part is that the geographical of the sampled point sits
Mark, another part is the WiFi signal intensity (RSSI) received in the sampled point.Using k-means clustering algorithm to all samplings
Point carries out clustering processing and generates final WiFi signal fingerprint base；Online WiFi fingerprint matching is the WiFi signal that will be measured in real time
Final positioning result is obtained compared with the information in WiFi fingerprint map using matching algorithm, WiFi fingerprint matching algorithm is base
In k neighbour (KNN) algorithm of machine learning.Under normal conditions, WiFi positioning accuracy increases with WiFi signal number of nodes is received
And increase, but excessive WiFi number of nodes can increase the difficulty for establishing WiFi fingerprint map and online WiFi fingerprint matching
Calculation amount, so, WiFi takes family positioning when positioning nearby 3 to 5 WiFi signal nodes are more appropriate.
The fixed height of three, floors:
Floor is determined high part and is made of barometer and WiFi positioning, and the corresponding height above sea level of floor is pre-deposited in mobile phone,
Observation atmospheric pressure value is obtained using barometer built in mobile phone, can substantially calculate height above sea level.Inside the location information of WiFi positioning
Also there is elevation information, the two altitude data is averaged according to pedestrian's state information weights, it is higher to be fused into a precision
Altitude data.It is influenced by extraneous and mobile phone itself, the height above sea level accuracy that barometer resolves is poor, in height above sea level
WiFi positioning result is more believed on altitude information, and height above sea level height above sea level corresponding with the number of floor levels pre-deposited is compared, is obtained
Specific story height.
Four, specific steps:
(1) three-dimensional WiFi fingerprint base map is established using WiFi feature in mobile phone collection room in advance.To simplify the calculation, it builds
Cube method is clustering method, if WiFi fingerprint base cluster is exactly that sampled point is divided into a class using the cluster feature of sampled point.
Specific fingerprint cluster feature are as follows: k sampling point object of random selection, each object initially represent the center of a cluster,
It is assigned to by nearest cluster at a distance from each cluster center according to it to remaining each sampled point, recalculates the flat of each cluster
Mean value constantly repeats this process, until the sum of the distance E of all sampled points to respective center converges to minimum, i.e., before and after twice
It calculates E difference and is less than given threshold value.The calculation formula of E isWherein, l indicates the ground of sampled point
It manages coordinate P=(x, y, z), cciIt is cluster ciAverage value.
(2) WiFi feature in mobile phone collection room carries out WiFi fingerprint using the WiFi fingerprint base map established before
Match, obtains personnel's initial position.WiFi finger print matching method is space k nearest neighbor method (KNN).
(3) mobile phone acquisition built-in acceleration counts, and judges whether pedestrian is kept in motion using relevant function method.Phase
Closing analytic approach is to calculate the standard deviation of acceleration, and calculation formula isWherein, a indicates that three axis always accelerate
Degree, σ, u respectively indicate total acceleration sequence { a1,a2,…,aNStandard deviation and mean value, when standard deviation be greater than 0.5 when, be spaced
The maximum value a of sampled acceleration in 1smaxWith minimum value aminAverage value as dynamic threshold, calculate acceleration related coefficient
ρ, calculation formula areWherein, σ (i) and u (i) difference table
Show the dead-center position of the i-th step to the total acceleration sequence { a of i+1 step dead-center position1,a2,…,aNStandard deviation and mean value.a
(i, k) indicates k-th of total acceleration value in the i-th step, and a (i-1, k) indicates k-th of total acceleration value in the (i-1)-th step.Currently
When the sequence length of two steps is inconsistent afterwards, short sequence is filled acceleration information by algorithm backward keeps length consistent, is week with 1s
The related coefficient of phase statistics movement and non-athletic two states, when related coefficient is greater than 0.7, it is believed that personnel are in movement shape
State.
(4) if personnel are kept in motion, mobile phone acquires built-in acceleration meter, gyroscope and magnetometer data, utilizes PDR
Algorithm resolves pedestrian's cadence, step-length and course, and then is calculated by the step-length and course of initial position message and next step next
The location information of step, is repeated in, the location information of each step after obtaining.Cadence and step-length are resolved by acceleration information, inspection
Three axis total acceleration sizes are surveyed, using the cyclically-varying principle of personnel's walking brief acceleration data, are examined using zero crossing peak value
Survey method calculates pedestrian's cadence, estimates pedestrian's step-length using non-linear step-length model, non-linear step-length model formation isWherein, dkIndicate step-length when kth step, AmaxAnd AminThree axis respectively indicated in a step always accelerate
It spends maximum value and minimum value, λ is model coefficient, obtained before positioning by least square in training.Course information is by expansion card
Kalman Filtering (EKF) algorithm fusion acceleration, gyroscope and magnetometer data obtain, and calculation accuracy is higher.Firstly, utilizing quaternary
Number method resolves three-axis gyroscope data, the rigid block element expression matrix form of quaternary number are as follows:Wherein, Q=[q0,q1,q2,q3]T, it is a quaternionic vector, q0
It is the real part of quaternary number, q1、q2、q3It is the imaginary part of quaternary number, ω=[ωx,ωy,ωz]TIt is the number of three axis of gyroscope x, y, z
Value.Then, each sensing data is merged using EKF, corrects quaternary number Q value, the state equation and measurement equation of EKF is respectively
Qk+1=FQk+wkWithWherein,It is that state turns
Move matrix, wkIt is process noise.It is posture spin matrix, g is normalized gravitational vectors, and h is normalized magnetic field
Strength vector.avk+1And mvk+1It is the measurement noise of accelerometer and magnetometer respectively, is incoherent zero-mean white noise.
Finally, course angle be calculated being
(5) mobile phone acquisition air pressure counts, and calculates elevation information, in conjunction with two-dimensional position information obtained in the previous step, obtains
To three dimensional local information.Height calculation formula are as follows:Wherein, p is the atmospheric pressure value measured, p0
For standard atmospheric pressure value 101.325kPa.
(6) it using WiFi positioning result and PDR location data before the fusion of Unscented kalman filtering (UKF) method, obtains
Final three-dimensional localization result.UKF measures equation are as follows:Wherein, Uk
Indicate final three dimensional local information；xk、yk、zkRespectively indicate pedestrian's kth step three-dimensional coordinate position that WiFi is positioned；xk-1、
yk-1Respectively indicate -1 step two-dimensional coordinate position of pedestrian's kth that WiFi is positioned；ψkIndicate the course angle that pedestrian walks in kth, Vk
For the systematic observation noise of 6 dimensions.
Claims (3)
1. a kind of personnel's indoor orientation method based on mobile phone sensor and WiFi feature, which comprises in collection room
WiFi feature obtains the initial position of personnel with the WiFi fingerprint base map match pre-established；Pedestrian is resolved using PDR algorithm
Pedestrian movement's state, cadence, step-length, course and obtain the location information of each step of pedestrian；It is counted in conjunction with mobile phone acquisition air pressure
According to and calculated elevation information, obtain the preliminary three dimensional local information of pedestrian；WiFi feature is acquired to preliminary three by mobile phone
Dimension location information is corrected, and obtains the final three dimensional local information of pedestrian；It is characterized in that the final three dimensional local information
It is calculated and is obtained by following mathematical expressions:Wherein: UkIndicate final three-dimensional position
Information；xk、yk、zkRespectively indicate pedestrian's kth step three-dimensional coordinate position that WiFi is positioned；xk-1、yk-1It is fixed to respectively indicate WiFi
- 1 step two-dimensional coordinate position of pedestrian's kth that position obtains；ψkIndicate the course angle that pedestrian walks in kth, VkSystematic observation for 6 dimensions is made an uproar
Sound.
2. according to the method described in claim 1, wherein in judgement pedestrian movement's state step in the PDR algorithm, first
Definition: a indicates three axis total accelerations, and σ, u respectively indicate total acceleration sequence { a1,a2,…,aNStandard deviation and mean value, σ (i)
The dead-center position of the i-th step is respectively indicated to the total acceleration sequence { a of i+1 step dead-center position with u (i)1,a2,…,aNMark
Quasi- difference and mean value, σ (i-1) and u (i-1) respectively indicate the dead-center position of the (i-1)-th step to the total acceleration sequence of the i-th step dead-center position
Arrange { a1,a2,…,aNStandard deviation and mean value, a (i, k) indicate the i-th step in k-th of total acceleration value, a (i-1, k) indicate
K-th of total acceleration value in (i-1)-th step；Then the correlation coefficient ρ of three axis total accelerations is calculated, calculation formula isWhen related coefficient is greater than 0.7, judge that personnel are in fortune
Dynamic state.
3. according to the method described in claim 1, wherein pedestrian's step-length in the PDR algorithm is estimated using non-linear step-length model
It obtains, model formation isWherein, dkIndicate step-length when kth step, AmaxAnd AminIt respectively indicates
Three axis total acceleration maximum values and minimum value, λ in one step are model coefficients, are obtained before positioning by least square in training
?.
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| CN110933599A (en) * | 2019-12-17 | 2020-03-27 | 北京理工大学 | Self-adaptive positioning method fusing UWB and WIFI fingerprints |
| CN111006668A (en) * | 2019-12-10 | 2020-04-14 | 郑州联睿电子科技有限公司 | Three-dimensional positioning method based on ultra-wideband and barometric sensor fusion |
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| CN110375741A (en) * | 2019-07-09 | 2019-10-25 | 中移（杭州）信息技术有限公司 | Pedestrian's dead reckoning method and terminal |
| 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 |
| CN112902960A (en) * | 2019-12-04 | 2021-06-04 | 中移(上海)信息通信科技有限公司 | Indoor positioning method, device, equipment and storage medium |
| CN111006668A (en) * | 2019-12-10 | 2020-04-14 | 郑州联睿电子科技有限公司 | Three-dimensional positioning method based on ultra-wideband and barometric sensor fusion |
| CN111006668B (en) * | 2019-12-10 | 2023-07-07 | 郑州联睿电子科技有限公司 | Three-dimensional positioning method based on ultra-wideband and air pressure sensor fusion |
| CN110933599A (en) * | 2019-12-17 | 2020-03-27 | 北京理工大学 | Self-adaptive positioning method fusing UWB and WIFI fingerprints |
| CN111722180A (en) * | 2020-07-02 | 2020-09-29 | 广东工业大学 | Kalman filtering-based indoor pedestrian positioning method, device and system |
| CN111722180B (en) * | 2020-07-02 | 2021-08-13 | 广东工业大学 | Kalman filtering-based indoor pedestrian positioning method, device and system |
| CN113256866A (en) * | 2021-06-15 | 2021-08-13 | 南京高美吉交通科技有限公司 | Urban rail transit barrier-free passing system and implementation method thereof |
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