CN106093994B - A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering - Google Patents
A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering Download PDFInfo
- Publication number
- CN106093994B CN106093994B CN201610372937.0A CN201610372937A CN106093994B CN 106093994 B CN106093994 B CN 106093994B CN 201610372937 A CN201610372937 A CN 201610372937A CN 106093994 B CN106093994 B CN 106093994B
- Authority
- CN
- China
- Prior art keywords
- imu
- rfid
- gnss
- positioning
- estimation
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
Abstract
The present invention relates to a kind of multi-source joint positioning methods based on adaptive weighted mixing Kalman filtering.This method is combined positioning by GNSS/RFID/IMU.This method, by searching for weight database, can be weighted adaptive Kalman filter to the location information of IMU and RFID, export more accurate positioning in GNSS signal losing lock.Multi-source joint positioning method of the present invention based on adaptive weighted mixing Kalman filtering, the positioning accuracy of GNSS is improved using expanded Kalman filtration algorithm；The positioning accuracy that blind area is improved using the algorithm of multisensor alignment by union is overcome skyscraper and blocks bring influence；By establishing weight data library, the difficulty of weight calculation is reduced, arithmetic speed is improved.
Description
Technical field
The present invention relates to a kind of multi-source joint positioning methods based on adaptive weighted mixing Kalman filtering, belong to multi-source
The technical field of alignment by union.
Background technique
The method of Multi-source Information Fusion has been widely used in space flight and aviation engineering, safety of maritime navigation, Navigation of Pilotless Aircraft and has determined
The fields such as position.Single sensor is spatially only capable of some specific region in overlay environment, and if being used alone one
Sensor, whole system may cause the loss of metric data because of single sensor fault, so as to cause whole system paralysis
Paralysis or collapse, influence positioning accuracy, thus multisensor alignment by union played in terms of improving positioning accuracy it is more and more important
Effect.
Existing main location technology has following several: (one) Global Navigation Satellite System (Global Navigation
Satellite System, GNSS), refer to the global positioning system (GPS) that all satellite navigation systems include the U.S., Russia sieve
This GLONASS satellite system (GLONASS), the Galileo system (Galileo) of European Union, China dipper system
(COMPASS), it can provide the information such as three-dimensional position, speed in real time for the army and the people user, but there is blind location area letters
Number missing the problem of.(2) radio frequency identification (RFID) is to track and position specific field using existing Radio Network System
The RF of conjunction is identified.Receiver is by handling the signal received, to position the equipment.Although RFID has price just
Preferably, the advantages that short distance precision is high, but with the increase of orientation distance, positioning accuracy will receive very big influence.(3) it is used to
Property navigation elements (IMU) positioning positioned using accelerometer and gyroscope, do not need auxiliary signal base station, have completely from
The advantages that main, free from the influence of the external environment, but IMU haves the shortcomings that position error can add up as time goes by.
Many scholars at home and abroad have in-depth study to multi-source alignment by union technology.Chinese patent CN1908587 is disclosed
The location information of GPS and DR are sent to microcontroller by a kind of GPS and DR Vehicle Integrated Location System and localization method, this method respectively
The Fuzzy Combined Kalman filter based on genetic Optimization Algorithm in device, exports location information after fusion treatment.China
The vehicle locating device of RFID and GPS are had studied in patent CN202631740U, the device is by reading the standard in electronic tag
It firmly believes breath, calculates GPS error, realize the accurate positionin of vehicle.The IMU of low cost is had studied in Chinese patent CN101319902
With the integrated positioning orienting device of GPS.The information that the device resolves the GPS information measured and IMU is merged by information
Processing, obtains the optimal navigation information of carrier.But above-mentioned positioning device and method still can not solve or avoid each positioning
The shortcomings that technology itself and deficiency.
Kalman filtering algorithm is common algorithm in navigation positioning system.Kalman filtering is by computer implemented reality
When recursive algorithm, it estimates all signals to be treated according to system state equation and observational equation.Common Kalman
Filtering is discussed under the premise of system linear, and in practice, system be constantly present it is different degrees of non-linear, with expansion
Exhibition Kalman filtering (EKF) can effectively solve the problems, such as nonlinear filtering, realize more accurate positioning.About extension karr
The particular contents such as the derivation of graceful algorithm, with reference to Fu Mengyin, Deng Zhihong et al. write " Kalman filter is theoretical and its in navigation is
Application in system ".
Summary of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of multi-source connection based on adaptive weighted mixing Kalman filtering
Close localization method.
Summary of the invention:
Positioning is combined with GNSS/RFID/IMU.This method can be in GNSS signal losing lock, by searching for weighted data
Library is weighted adaptive Kalman filter to the location information of IMU and RFID, exports more accurate positioning.
Location algorithm, main includes two aspects: 1) when GNSS signal reception good, respectively to GNSS, RFID and IMU
Metrical information is extended Kalman filtering, while establishing the weight database of RFID and IMU；2) when GNSS signal lacks,
The metrical information of RFID and IMU are put into extended Kalman filter first, obtain filtering information, searches for weight database, it will
The GNSS filtering and calibration information that filtered information and date library prestores is matched according to minimum error principle, is determined optimal
The weight of RFID and IMU is finally weighted information fusion to the data of RFID and IMU, exports optimal location information.
Term explanation:
Reader: reader is the component part of RFID location system, plays the ID number or data zone content for reading label
Effect.
Reference point: reference point is the component part of GNSS positioning system, by the distance of witness mark and mobile object,
Play the role of improving positioning accuracy.
Label: label is the component part of RFID location system, plays the role of mark identification, information collection.
Receiver: playing the role of reception, tracking, transformation and measuring signal, provides initial data for target positioning.
The technical solution of the present invention is as follows:
A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering, comprising the following steps:
1) receiver receives the signal of GNSS, RFID, IMU respectively, judges whether GNSS signal is interfered；
If 2) GNSS signal is not affected by interference, to position the coordinate of targetFor state vector, withGNSS system motion model and GNSS observation model are established for observation vector, is filtered by spreading kalman
The estimation of GNSS state optimization is calculated in wave deviceWherein r1、...、rnIt is the positioning of GNSS measurement respectively
Distance of the coordinate of target to n base station；xk,ykRespectively under WGS-84 coordinate, the k moment, the east orientation coordinate of gauss projection plane
With north orientation coordinate；n≥2；
3) to position the coordinate of targetFor state vector, withFor
RFID observation vector is established RFID system motion model and RFID observation model, is calculated by extended Kalman filter
The estimation of RFID state optimizationWherein,It is the positioning mesh measured by RFID respectively
The reader of carrying is marked to the distance of m base station；m≥3；
4) to position the coordinate of targetFor state vector, withFor IMU observe to
Amount, establishes IMU system motion model and IMU observation model, IMU state optimization is calculated by extended Kalman filter and estimates
MeterWherein, w, vy、vxRespectively pass through the course angle of the positioning target of IMU measurement, the positioning direction target x
Speed, position the direction target y speed；
5) basisDetermine RFID's and IMU
Weight coefficientAnd the GNSS state optimization at each moment is estimated, the weight coefficient of RFID and IMUIt is stored in database；WhereinWhen being that GNSS signal is not affected by interference, the k moment it is complete
Office's optimum state；
6) when GNSS signal is interfered, by RFID and IMU integrated navigation to positioning target positioning；By RFID and
The location information of IMU is respectively put into extended Kalman filter, obtains the optimal estimation of RFIDWith IMU's
Optimal estimation
7) weight coefficient of RFID and the weight coefficient of IMU are searched in the database respectively；
8) information fusion is weighted according to error minimum principle to the optimal estimation of the optimal estimation of RFID and IMU, obtained
To the global state optimal estimation at i moment
Preferably, the formula of system motion model:
Xk=f (Xk-1,k-1)+Γ(Xk-1,k-1)Wk-1
Wherein,dkIt is the displacement for the k-1 moment positioning target being carved to k, wk-1For
The course angle of the positioning target of k-1 moment IMU measurement；Γ(Xk-1, k-1) and it is noise inputs function, { Wk-1It is zero-mean system
White noise sequence, E [WkWj T]=Qkδkj, QkIt is the variance of system noise sequence；
Preferably, the formula of GNSS observation model:
Wherein,(x1,y1)、...、(xn,yn) it is to participate in positioning respectively
The coordinate of n base station； It is zero-mean observation white noise sequence,Its
In,It is the variance of observation noise sequence；
WithIt is incoherent white noise sequence；It is the zero-mean system white noise of GNSS；
Preferably, the step of Extended Kalman filter is as follows:
A, system motion model and observation model are linearized；
B, according to the system motion model of linearisation and the observation model of linearisation, in conjunction with system noise covariance QkAnd sight
Survey noise covariance Rk, state optimization estimation and the covariance matrix at k-1 moment are given, according to Extended Kalman filter equation meter
It calculates and obtains the state optimization estimation at k momentWith covariance matrix Pk.State optimization estimation and covariance matrix
Use recursive algorithm, it is described it is " given " assume the estimation of k-1 moment state optimization and covariance matrix be it is known, lead to
Crossing recursive algorithm can determine state optimization estimation and the covariance matrix at subsequent time k moment.
It is further preferred that the mistake of GNSS state optimization estimation is calculated in step 2) by extended Kalman filter
Journey is as follows:
The linearisation of GNSS system motion model: system state equation is unfolded according to Taylor's formula around filter value:
Higher order term is omitted to obtain:Wherein:
The linearisation of GNSS observation model:Wherein
The estimation of GNSS state optimizationWith the covariance matrix P of estimationk GSolution procedure it is as follows:
State one-step prediction:
One-step prediction covariance matrix
Filtering gain
The estimation of GNSS state optimization
Covariance matrix
It is further preferred that RFID state optimization is estimated in step 3)With the covariance square of estimation
Battle arrayCalculating process it is as follows:
RFID system motion model:
RFID observation model:
Wherein, It is m mark respectively
The coordinate of label；
The estimation of k-1 moment RFID state optimizationRFID estimation covariance matrix beIt is logical
It crosses extended Kalman filter and the estimation of k moment RFID state optimization is calculatedThe covariance square of estimation
Battle array be
It is further preferred that IMU state optimization is estimated in step 4)With the covariance matrix of estimationCalculating process it is as follows:
IMU system motion model:
IMU state observation model:
Wherein, Δ T is the time difference at k moment Yu k-1 moment；
The estimation of k-1 moment IMU state optimizationIMU estimation covariance matrix bePass through
The IMU state optimization estimation at k moment is calculated in extended Kalman filterThe covariance matrix of estimation
Preferably, the method that the weight coefficient of RFID and the weight coefficient of IMU are determined in step 5) is as follows:
The weight coefficient of k moment RFID:
The weight coefficient of k moment IMU:
Preferably, in step 7) in the database respectively search for RFID weight coefficient and IMU weight coefficient it is specific
Method is as follows:
Wherein, i=1,2 ...
K ... in search databaseDetermination makesIt is the smallestThen its is correspondingFor the weight of RFID
The weight coefficient of coefficient and IMU.
Preferably, global state optimal estimation is determined in step 10)The specific method is as follows:
Initially set up target error function:
Make target error functionIt is the smallestFor global state optimal estimation:
It solves equation:
Fused global state optimal estimationP is estimated with error optimizationoIt is respectively as follows:
Global state optimal estimation is the i moment when GNSS does not have signal, the final corrected rear positioning letter exported
Breath.
Preferably, the course angle in IMU is measured according to gyroscope.
The invention has the benefit that
1. the multi-source joint positioning method of the present invention based on adaptive weighted mixing Kalman filtering, utilizes expansion card
The positioning accuracy of Kalman Filtering algorithm raising GNSS；The positioning accurate of blind area is improved using the algorithm of multisensor alignment by union
Degree overcomes skyscraper and blocks bring influence；By establishing weight data library, the difficulty of weight calculation is reduced, is improved
Arithmetic speed；
2. the multi-source joint positioning method of the present invention based on adaptive weighted mixing Kalman filtering, uses GNSS/
RFID/IMU is combined positioning, in GNSS signal losing lock, by searching for weight database, believes the positioning of IMU and RFID
Breath is weighted adaptive Kalman filter, exports more accurate positioning.
Detailed description of the invention
Fig. 1 is the flow diagram of the method for the invention；
Fig. 2 is that GNSS signal is not affected by the flow diagram that database is established when interference；
Fig. 3 is that GNSS signal is interfered constantly, is shown by process of the RFID and IMU integrated navigation to positioning target positioning
It is intended to.
Specific embodiment
Below with reference to embodiment and Figure of description, the present invention will be further described, but not limited to this.
Embodiment 1
As shown in Figs. 1-3.
A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering, comprising the following steps:
1) receiver receives the signal of GNSS, RFID, IMU respectively, judges whether GNSS signal is interfered；
If 2) GNSS signal is not affected by interference, to position the coordinate of targetFor state vector, withGNSS system motion model and GNSS observation model are established for observation vector, is filtered by spreading kalman
The estimation of GNSS state optimization is calculated in wave deviceWherein r1、r2、r3It is the positioning mesh of GNSS measurement respectively
Distance of the target coordinate to three base stations；xk,ykRespectively under WGS-84 coordinate, the k moment, the east orientation coordinate of gauss projection plane
With north orientation coordinate；
3) to position the coordinate of targetFor state vector, withFor
RFID observation vector is established RFID system motion model and RFID observation model, is calculated by extended Kalman filter
The estimation of RFID state optimizationWherein,It is to be determined by RFID measurement respectively
Distance of the reader that position target carries to four base stations；
4) to position the coordinate of targetFor state vector, withFor IMU observe to
Amount, establishes IMU system motion model and IMU observation model, IMU state optimization is calculated by extended Kalman filter and estimates
MeterWherein, w, vy、vxRespectively pass through the course angle of the positioning target of IMU measurement, the positioning direction target x
Speed, position the direction target y speed；
5) basisDetermine RFID's and IMU
Weight coefficientAnd the GNSS state optimization at each moment is estimated, the weight coefficient of RFID and IMUIt is stored in database；WhereinWhen being that GNSS signal is not affected by interference, the overall situation at k moment
Optimum state；
6) when GNSS signal is interfered, by RFID and IMU integrated navigation to positioning target positioning；By RFID and
The location information of IMU is respectively put into extended Kalman filter, obtains the optimal estimation of RFIDWith IMU's
Optimal estimation
7) weight coefficient of RFID and the weight coefficient of IMU are searched in the database respectively；
8) information fusion is weighted according to error minimum principle to the optimal estimation of the optimal estimation of RFID and IMU, obtained
To the global state optimal estimation at i moment
Embodiment 2
Multi-source joint positioning method as described in Example 1 based on adaptive weighted mixing Kalman filtering, difference
It is, the formula of GNSS system motion model:
Wherein,dkIt is the displacement for the k-1 moment positioning target being carved to k, wk-1For
The course angle of the positioning target of k-1 moment IMU measurement；It is noise inputs function,It is zero-mean system
White noise sequence, It is the variance matrix of system noise sequence.
Embodiment 3
Multi-source joint positioning method as described in Example 1 based on adaptive weighted mixing Kalman filtering, difference
It is, the formula of GNSS observation model:
Wherein,(x1,y1)、(x2,y2)、(x3,y3) it is to participate in determining respectively
The coordinate of 3 base stations of position；r1、r2、r3It is the coordinate of the positioning target of GNSS measurement respectively to three
The distance of base station；It is zero-mean observation white noise sequence,Wherein,It is observation noise sequence
Variance.
Embodiment 4
Multi-source joint positioning method as described in Example 1 based on adaptive weighted mixing Kalman filtering, difference
It is, the step of Extended Kalman filter is as follows:
A, system motion model and observation model are linearized；
B, according to the system motion model of linearisation and the observation model of linearisation, in conjunction with system noise covariance QkAnd sight
Survey noise covariance Rk, state optimization estimation and the covariance matrix at k-1 moment are given, according to Extended Kalman filter equation meter
It calculates and obtains the state optimization estimation at k momentWith covariance matrix Pk.State optimization estimation and covariance matrix
Use recursive algorithm, it is described it is " given " assume the estimation of k-1 moment state optimization and covariance matrix be it is known, lead to
Crossing recursive algorithm can determine state optimization estimation and the covariance matrix at subsequent time k moment.
Embodiment 5
Multi-source joint positioning method as described in Example 4 based on adaptive weighted mixing Kalman filtering, difference
It is, the process that the estimation of GNSS state optimization is calculated by extended Kalman filter in step 2) is as follows:
The linearisation of GNSS system motion model: system state equation is unfolded according to Taylor's formula around filter value:
Higher order term is omitted to obtain:Wherein:
The linearisation of GNSS observation model:Wherein
The estimation of GNSS state optimizationWith the covariance matrix P of estimationk GSolution procedure it is as follows:
State one-step prediction:
One-step prediction covariance matrix
Filtering gain
The estimation of GNSS state optimization
Covariance matrix
Embodiment 6
Multi-source joint positioning method as described in Example 4 based on adaptive weighted mixing Kalman filtering, difference
It is, RFID state optimization is estimated in step 3)With the covariance matrix of estimationCalculating process such as
Under:
RFID system motion model:
RFID observation model:
Wherein,
It is the coordinate of four labels respectively；
The estimation of k-1 moment RFID state optimizationRFID estimation covariance matrix beIt is logical
It crosses extended Kalman filter and the estimation of k moment RFID state optimization is calculatedThe covariance square of estimation
Battle array be
Embodiment 7
Multi-source joint positioning method as described in Example 4 based on adaptive weighted mixing Kalman filtering, difference
It is, IMU state optimization is estimated in step 4)With the covariance matrix of estimationCalculating process it is as follows:
IMU system motion model:
IMU state observation model:
Wherein, Δ T is the time difference at k moment Yu k-1 moment；
The estimation of k-1 moment IMU state optimizationIMU estimation covariance matrix bePass through
The IMU state optimization estimation at k moment is calculated in extended Kalman filterThe covariance matrix of estimation
Embodiment 8
Multi-source joint positioning method as described in Example 1 based on adaptive weighted mixing Kalman filtering, difference
It is, the method that the weight coefficient of RFID and the weight coefficient of IMU are determined in step 5) is as follows:
The weight coefficient of k moment RFID:
The weight coefficient of k moment IMU:
Embodiment 9
Multi-source joint positioning method as described in Example 4 based on adaptive weighted mixing Kalman filtering, difference
It is, the specific method is as follows for the weight coefficient of the weight coefficient of search RFID and IMU respectively in the database in step 7):
Wherein, i=1,2 ...
K ... in search databaseDetermination makesIt is the smallestThen its is correspondingFor the weight of RFID
The weight coefficient of coefficient and IMU.
Embodiment 10
Multi-source joint positioning method as described in Example 4 based on adaptive weighted mixing Kalman filtering, difference
It is, global state optimal estimation is determined in step 10)The specific method is as follows:
Initially set up target error function:
Make target error functionIt is the smallestFor global state optimal estimation:
It solves equation:
Fused global state optimal estimationP is estimated with error optimizationoIt is respectively as follows:
Global state optimal estimation is the i moment when GNSS does not have signal, the final corrected rear positioning letter exported
Breath.
Claims (4)
1. a kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering, which is characterized in that including following
Step:
1) receiver receives the signal of GNSS, RFID, IMU respectively, judges whether GNSS signal is interfered；
If 2) GNSS signal is not affected by interference, to position the coordinate of targetFor state vector, withGNSS system motion model and GNSS observation model are established for observation vector, passes through Extended Kalman filter
The estimation of GNSS state optimization is calculated in deviceWherein r1、...、rnIt is the positioning mesh of GNSS measurement respectively
Distance of the target coordinate to n base station；xk,ykRespectively under WGS-84 coordinate, the k moment, the east orientation coordinate of gauss projection plane and
North orientation coordinate；n≥2；
3) to position the coordinate of targetFor state vector, withFor RFID sight
Direction finding amount establishes RFID system motion model and RFID observation model, and RFID shape is calculated by extended Kalman filter
State optimal estimationWherein,It is to be carried by the positioning target of RFID measurement respectively
Reader to m base station distance；m≥3；
4) to position the coordinate of targetFor state vector, withFor IMU observation vector,
IMU system motion model and IMU observation model are established, the estimation of IMU state optimization is calculated by extended Kalman filterWherein, w, vy、vxRespectively by the course angle of the positioning target of IMU measurement, position target x direction
Speed, the speed for positioning the direction target y；
5) basisDetermine the weight system of RFID and IMU
NumberAnd the GNSS state optimization at each moment is estimated, the weight coefficient of RFID and IMU
It is stored in database；WhereinWhen being that GNSS signal is not affected by interference, global optimum's state at k moment；
6) when GNSS signal is interfered, by RFID and IMU integrated navigation to positioning target positioning；By RFID's and IMU
Location information is respectively put into extended Kalman filter, obtains the optimal estimation of RFIDIt is optimal with IMU
Estimation
7) weight coefficient of RFID and the weight coefficient of IMU are searched in the database respectively；
8) information fusion is weighted according to error minimum principle to the optimal estimation of the optimal estimation of RFID and IMU, when obtaining i
The global state optimal estimation at quarter
2. the multi-source joint positioning method according to claim 1 based on adaptive weighted mixing Kalman filtering, special
The step of sign is, Extended Kalman filter is as follows:
A, system motion model and observation model are linearized；
B, according to the system motion model of linearisation and the observation model of linearisation, in conjunction with system noise covariance QkIt makes an uproar with observation
Sound covariance Rk, state optimization estimation and the covariance matrix at k-1 moment are given, is obtained according to Extended Kalman filter equation calculation
State optimization to the k moment is estimatedWith covariance matrix Pk。
3. the multi-source joint positioning method according to claim 1 based on adaptive weighted mixing Kalman filtering, special
Sign is that the method that the weight coefficient of RFID and the weight coefficient of IMU are determined in step 5) is as follows:
The weight coefficient of k moment RFID:
The weight coefficient of k moment IMU:
4. the multi-source joint positioning method according to claim 1 based on adaptive weighted mixing Kalman filtering, special
Sign is that the specific method is as follows for the weight coefficient of the weight coefficient of search RFID and IMU respectively in the database in step 7):
Wherein, i=1,2 ... k ... are searched
In rope databaseDetermination makesIt is the smallestThen its is correspondingFor RFID weight coefficient and
The weight coefficient of IMU.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610372937.0A CN106093994B (en) | 2016-05-31 | 2016-05-31 | A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610372937.0A CN106093994B (en) | 2016-05-31 | 2016-05-31 | A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106093994A CN106093994A (en) | 2016-11-09 |
| CN106093994B true CN106093994B (en) | 2019-03-29 |
Family
ID=57230925
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610372937.0A Active CN106093994B (en) | 2016-05-31 | 2016-05-31 | A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106093994B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106767796B (en) * | 2017-01-23 | 2020-02-21 | 北京优尔伯特创新科技有限公司 | Fusion algorithm of unmanned ship distance measuring unit and inertia measuring unit in aqueduct-like environment |
| CN108040324B (en) * | 2017-11-16 | 2020-05-01 | 南方科技大学 | Positioning method and positioning system of rescue capsule robot |
| CN107727079B (en) * | 2017-11-30 | 2020-05-22 | 湖北航天飞行器研究所 | Target positioning method of full-strapdown downward-looking camera of micro unmanned aerial vehicle |
| CN109048890B (en) * | 2018-07-13 | 2021-07-13 | 哈尔滨工业大学（深圳） | Robot-based coordinated trajectory control method, system, device and storage medium |
| CN108645415A (en) * | 2018-08-03 | 2018-10-12 | 上海海事大学 | A kind of ship track prediction technique |
| CN108827318A (en) * | 2018-08-20 | 2018-11-16 | 中科物栖（北京）科技有限责任公司 | Unmanned plane indoor orientation method and device |
| CN110146074B (en) * | 2018-08-28 | 2021-06-15 | 北京初速度科技有限公司 | Real-time positioning method and device applied to automatic driving |
| CN109282808B (en) * | 2018-11-23 | 2021-05-04 | 重庆交通大学 | Unmanned aerial vehicle and multi-sensor fusion positioning method for bridge three-dimensional cruise detection |
| CN109556614B (en) * | 2018-12-14 | 2020-02-21 | 北京百度网讯科技有限公司 | Positioning method and device for unmanned vehicle |
| CN109946723B (en) * | 2019-03-07 | 2022-05-03 | 深圳开阳电子股份有限公司 | Adaptive extended Kalman tracking method and device and storage medium |
| CN110083890B (en) * | 2019-04-10 | 2021-02-02 | 同济大学 | Intelligent automobile tire radius self-adaptive estimation method based on cascading Kalman filtering |
| CN110955263A (en) * | 2019-12-31 | 2020-04-03 | 中国电子科技集团公司信息科学研究院 | Active sensing and autonomous approaching method of mobile robot and mobile robot system |
| CN113311458A (en) * | 2021-06-17 | 2021-08-27 | 东南大学 | Indoor pseudo-satellite fingerprint matching and carrier phase ranging combined positioning method |
| CN113504558B (en) * | 2021-07-14 | 2024-02-27 | 北京理工大学 | Ground unmanned vehicle positioning method considering road geometric constraint |
| CN113804202B (en) * | 2021-11-19 | 2022-02-22 | 智道网联科技(北京)有限公司 | Integrated navigation method, electronic device and storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080039991A1 (en) * | 2006-08-10 | 2008-02-14 | May Reed R | Methods and systems for providing accurate vehicle positioning |
| CN101319902B (en) * | 2008-07-18 | 2010-09-08 | 哈尔滨工程大学 | Low-cost combination type positioning and orienting device and combined positioning method |
| CN102494683B (en) * | 2011-10-26 | 2015-04-15 | 泰斗微电子科技有限公司 | Radio frequency identification (RFID)-based joint positioning device and method |
| CN202631740U (en) * | 2012-06-11 | 2012-12-26 | 福建师范大学 | Vehicle positioning device based on RFID (Radio Frequency Identification Device) and GPS (Global Positioning System) |
| CN103499350B (en) * | 2013-09-28 | 2016-01-27 | 长安大学 | Vehicle high-precision localization method and the device of multi-source information is merged under GPS blind area |
-
2016
- 2016-05-31 CN CN201610372937.0A patent/CN106093994B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN106093994A (en) | 2016-11-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106093994B (en) | A kind of multi-source joint positioning method based on adaptive weighted mixing Kalman filtering | |
| CN110645979B (en) | Indoor and outdoor seamless positioning method based on GNSS/INS/UWB combination | |
| CN108362281B (en) | Long-baseline underwater submarine matching navigation method and system | |
| EP2946167B1 (en) | Method and apparatus for determination of misalignment between device and pedestrian | |
| Ali et al. | Low-cost MEMS-based pedestrian navigation technique for GPS-denied areas | |
| CN106643792B (en) | Inertial Measurement Unit and geomagnetic sensor integral calibrating device and scaling method | |
| Ascher et al. | Integrity monitoring for UWB/INS tightly coupled pedestrian indoor scenarios | |
| CN102692179A (en) | Positioning device, positioning method, program, and recording medium | |
| CN110057354A (en) | One kind being based on the modified geomagnetic matching navigation method of magnetic declination | |
| Mostafa et al. | A novel GPS/RAVO/MEMS-INS smartphone-sensor-integrated method to enhance USV navigation systems during GPS outages | |
| KR101886932B1 (en) | Positioning system for gpr data using geographic information system and road surface image | |
| Dumble et al. | Airborne vision-aided navigation using road intersection features | |
| Long et al. | Single UWB anchor aided PDR heading and step length correcting indoor localization system | |
| CN109764870A (en) | Carrier initial heading evaluation method based on transformation estimator modeling scheme | |
| Antigny et al. | Pedestrian track estimation with handheld monocular camera and inertial-magnetic sensor for urban augmented reality | |
| CN108151765A (en) | Attitude positioning method is surveyed in a kind of positioning of online real-time estimation compensation magnetometer error | |
| Singh et al. | Ubiquitous hybrid tracking techniques for augmented reality applications | |
| Zheng et al. | Magnetic-based positioning system for moving target with feature vector | |
| CN108710145A (en) | A kind of unmanned plane positioning system and method | |
| CN109099905A (en) | A kind of list celestial body astrofix quickly, direct computing method | |
| CN111536976A (en) | Campus prevention and control system that shuttles back and forth | |
| Lategahn et al. | Robust pedestrian localization in indoor environments with an IMU aided TDoA system | |
| Groves et al. | Enhancing micro air vehicle navigation in dense urban areas using 3D mapping aided GNSS | |
| Lategahn et al. | Extended Kalman filter for a low cost TDoA/IMU pedestrian localization system | |
| CN109737957A (en) | A kind of INS/LiDAR Combinated navigation method and system using cascade FIR filtering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |