CN106908762B - A kind of more hypothesis UKF method for tracking target for UHF-RFID system - Google Patents
A kind of more hypothesis UKF method for tracking target for UHF-RFID system Download PDFInfo
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- CN106908762B CN106908762B CN201710021764.2A CN201710021764A CN106908762B CN 106908762 B CN106908762 B CN 106908762B CN 201710021764 A CN201710021764 A CN 201710021764A CN 106908762 B CN106908762 B CN 106908762B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/12—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves by co-ordinating position lines of different shape, e.g. hyperbolic, circular, elliptical or radial
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Abstract
A kind of more hypothesis UKF method for tracking target for UHF-RFID system, the following steps are included: step 1) is estimated to obtain the initial position of mobile robot using VIRE method, determine the label area where robot initial position, it is evenly arranged particle in region, and describes the original state of mobile robot by these particles；Step 2) collects the measurement information of each ultra-high frequency antenna, generates the location estimation of mobile robot as observation input, then each particle state is predicted and updated respectively using Unscented kalman filtering method, and calculates the weight of each particle；Step 3) screens it according to the weight of particle, then carries out resampling to it；Particle after step 4) fusion screening obtains the current state estimation of mobile robot, repeats step 1) -4), to realize the mobile robot tracking under UHF-RFID environment.The present invention has better positioning accuracy and convergence rate.
Description
Technical field
The present invention relates to localization for Mobile Robot field, mobile robot is fixed under especially a kind of UHF-RFID environment
The method of position.
Background technique
Wireless location is military activity and the necessary means for ensureing the normal traffic safety of the mankind.It is rapid for economic development
China, wireless location plays increasingly important role to society, as airport hall, exhibition room, warehouse, supermarket, library,
Underground parking, mine, the safe navigation of aviation and traffic control, the positioning and survey of the transportation dispatching of vehicle, spacecraft
The fields such as control, searching rescue, mobile communication will use wireless location.Positioning refers to determining object in a certain reference frame
Position.Therefore there are important application values.Common wireless location technology has an infrared ray, ultrasonic wave, WIFI, bluetooth,
UWB, ZIGBEE, RFID etc., wherein RFID can work in the presence of a harsh environment because of its strong antijamming capability, in view of its non-view
Away from non-contacting advantage, applied to positioning be able to achieve high-precision location requirement.
LANDMARC classics positioning system based on scene Recognition is adopted by the information of training and scene to positioning scene
Collection, and it is stored in database after resulting information is analyzed, using proximity principle come the tracking to realize mobile target.
However, to improve positioning accuracy under the scene, then need to increase the quantity of reference label.VIRE method is exactly not increase volume
In the case of outer reference label, the positioning accuracy of system is effectively improved.If the selection inaccuracy of VIRE method threshold value, will
It causes not generating common selection tag set in certain region, to cause the diverging of method.Particularly, in fuzzy map
In the case that compatible degree is unsatisfactory for requirement, the collapse of entire positioning system will lead to.At present in UHF-RFID positioning system, also
There is no technology that can realize by fusion virtual reference label and internal sensor (such as odometer, gyroscope, accelerometer etc.)
Mobile robot pose estimation, to improve the positioning accuracy of entire positioning system.
Summary of the invention
In order to overcome existing method error greatly and easily to dissipate and to virtual signal intensity value dependence caused by interpolation
The deficiency of too strong the problem of causing positioning to dissipate is guaranteeing the present invention provides a kind of to the adaptability of environment and the reality of positioning
Under the premise of when property, the UHF-RFID global localization methods based on more hypothesis UKF of positioning accuracy and robustness can be effectively improved, more
It is suitble to the positioning under the complex environment of interior to mobile robot.
The technical proposal for solving the technical problem of the invention is:
A kind of more hypothesis UKF method for tracking target for UHF-RFID system, the described method comprises the following steps:
Step 1) is estimated to obtain the initial position S (x of mobile robot using VIRE method0,y0), determine robot initial
Label area where position is evenly arranged particle in region, and describes the initial of mobile robot by these particles
State；
Step 2) collects the measurement information of each ultra-high frequency antenna, and the location estimation of mobile robot is generated by VIRE method
It is inputted as observation, then, each particle state is predicted and updated respectively using Unscented kalman filtering method UKF, and
Calculate the weight of each particle；
Step 3) screens it according to the weight of particle, then carries out resampling to it；
Particle after step 4) fusion screening obtains the current state estimation of mobile robot, repeats step 1)-
4), to realize the mobile robot tracking under UHF-RFID environment.
Further, in the step 1), the region where initial position is expressed as Ω, and M are then uniformly chosen in Ω
Particle, giving these particle original states isWherein To be randomly generated
, using these particle states as the original state of mobile robot.
Further, in the step 2), the measurement information is to estimate using VIRE algorithm to moving target position
Meter is as a result, primary uniform sampling, particle i original state component x, and y is estimated by VIRE method, gives any course angleInitially covariance matrix isWeightIt utilizes Obtain 2n
+ 1 sigma point.
In the step 2), system is observed zk=[xp,k yp,k]T, thenBy chi square distribution probability density function, each particle is obtained when each
The weight at quarterWherein, χ2(2) indicate that freedom degree is 2 chi square distribution, xp,k+1, yp,k+1Respectively the k+1 moment is
Overall view measures zk+1Coordinate components on the direction x and the direction y,WithRespectively particle i predicts observation at the k moment
AmountComponent on the direction x and the direction y,Respectively k+1 moment particle is observed on the direction x and the direction y
Covariance matrix.
In the step 3), when particle weightCast out the particle；Conversely, then claiming the particle effectively and retaining, have
Effect population is denoted as nc；Resampling will be carried out to particle simultaneously, as number of effective particles nc< η retainsParticle, and
?Variance is σ2Resampling is carried out in range, and number of effective particles is made to remain at η；It is adopted again conversely, then skipping
The sample stage.
In the step 4), the particle after screening, resampling is normalized, by using average weighted side
Particle after method fusion screening, to obtain mobile robot state estimation.
Beneficial effects of the present invention are mainly manifested in: under initial pose unknown situation, being realized under UHF-RFID environment
Localization for Mobile Robot.When conventionally employed VIRE method positions mobile robot, and not operatively mobile robot
Internal sensor information (such as odometer, gyroscope etc.).Positioning accuracy is largely dependent upon positioning locating for UHF-RFID
Environment, and the algorithm can not estimate pose of mobile robot angle.For this problem, the present invention provides one kind to be based on
The method for positioning mobile robot for assuming UKF, take full advantage of the information of UHF-RFID Yu robot interior sensor, effectively more
Ground overcomes the shortcomings that unstable single UKF filter and easy diverging.This method passes through VIRE algorithm for non-linear observation first
Systems with Linear Observation is converted into, then using the positioning assumed UKF method and realize mobile robot more.This method is compared to existing
VIRE object localization method, this method can effectively improve the positioning accuracy of system, and to pose of mobile robot angle carry out with
Track, meanwhile, relative to traditional UKF method, there is better positioning accuracy and convergence rate.
Detailed description of the invention
The scene arrangement schematic diagram of Fig. 1 hyperfrequency positioning system.
Fig. 2 uses band odometer moving trolley moving condition analysis chart.
The object locating system schematic diagram of Fig. 3 wireless sensor network.
The mono- hypothesis state estimation flow chart of Fig. 4.
Fig. 5 is directed to more hypothesis UKF Target Tracking System flow charts of UHF-RFID system.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.
Referring to Fig.1~Fig. 5, a kind of more hypothesis UKF method for tracking target for UHF-RFID system, the method include
Following steps:
The localizing environment of mobile robot under UHF-RFID is described in Fig. 1.1 represents ultra-high frequency antenna in figure, and 2 represent ginseng
Label is examined, 3 represent mobile robot.The localization region is 8*8, and region, which is included, is distributed in area to be positioned there are four ultra-high frequency antenna
Domain boundary, 16 reference labels, reference label are evenly arranged in monitor area as shown in figure 1.Wherein, four antennas pass through poll
Mode acquires the RSSI data of each reference label.Mobile robot carries odometer, mobile note.
As shown in connection with fig. 2, moveable robot movement model can be described as following state-space model:
xk+1=f (xk,uk)+wk+1 (10)
zk=Hxk+vk (11)
Wherein, k is discrete time, system mode xk=[xp,k,yp,k,θk]T。xp,k、yp,kRespectively k moment robot exists
Coordinate in x-axis and y-axis, θkFor attitude angle,For state-transition matrix.ukSystem input matrix, State Viewpoint
Survey zk=[xp,k yp,k]T, observing matrixwkFor process noise, zero-mean is obeyed, the height that variance matrix is Q
This distribution, vkFor systematic observation noise, the Gaussian Profile that zero-mean variance is R is obeyed.nXFor the dimension of system mode, nZTo see
The dimension of direction finding amount assumes the initial pose of mobile robot and initial variance is respectively x0|0And P0|0, and x0|0With wk, vkStatistics meaning
It is uncorrelated in justice.
As shown in figure 4, calculate particle one-step prediction state and variance, wherein population i=1,2 ... M,.
It updates
In conjunction with Fig. 4, in formula (13),For the Sigma point set that particle i takes at the k moment,It is that particle i takes
Status predication value of the Sigma point in k+1.Scaling parameter lambda=a2(n+ κ)-n must predict error for reducing, the choosing of a
The distribution for controlling sampled point is taken, κ is parameter to be selected, and specific value does not have boundary, usually to ensure matrixPositive semidefinite.It is averaged by formula (14)-(16) weighting, obtains system mode predicted value and status predication covariance
Matrix.The observation predicted value of formula (17) acquisition particle i.It is status predication value of the particle at the k+1 moment,Respectively
The weight of mean value and variance is calculated for Sigma point.It is i-th of particle in k moment status predication variance matrix, Q is system
Process noise covariance matrix.Convolution (18)-(20) can acquire the estimated value of particle i.
In conjunction with Fig. 5, at no point in the update process, zk+1For k+1 moment systematic perspective measured value,It predicts to see at the k+1 moment for particle
Measured value, H are observing matrix, and Matrix is newly ceased at the k+1 moment for particle.R respectively indicates
I particle newly ceases variance matrix and observation noise variance matrix at the k+1 moment.Indicate i-th of particle in the card at k+1 moment
Germania gain matrix,Indicate i-th of particle in the state estimation varivance matrix at k+1 moment.When particle weightCast out the particle.Conversely, then claiming the particle effectively and retaining, number of effective particles is denoted as nc, meanwhile, in order to avoid grain
Son is degenerated, and will carry out resampling to particle.As number of effective particles nc< η retainsParticle, and
(maximum weight particle) variance is σ2Resampling is carried out in range, and number of effective particles is made to remain at η.Conversely, then skipping
The resampling stage.
By using the particle after average weighted method fusion screening, resampling.
Wherein n is the population after screening, resampling,Estimate for mobile robot pose,It normalizes and weighs for particle
Value.
Claims (4)
1. a kind of more hypothesis UKF method for tracking target for UHF-RFID system, it is characterised in that: the method includes following
Step:
Step 1) is estimated to obtain the initial position S (x of mobile robot using VIRE method0,y0), determine robot initial position
The label area at place is evenly arranged particle in region, and the original state of mobile robot is described by these particles；
Step 2) collects the measurement information of each ultra-high frequency antenna, and the location estimation conduct of mobile robot is generated by VIRE method
Observation input, then, is predicted and is updated to each particle state respectively using Unscented kalman filtering method UKF, and calculated
The weight of each particle；
In the step 2), the measurement information is using VIRE algorithm to the estimated result of moving target position, initial grain
Sub- uniform sampling, particle i original state component x, y are estimated by VIRE method, give any course angleJust
Beginning covariance matrix isWeightUtilize particle i original state Obtain 2n+1
A sigma point；
In the step 2), system is observed zk=[xp,k yp,k]T, thenBy chi square distribution probability density function, each particle is obtained when each
The weight at quarterWherein, χ2(2) indicate that freedom degree is 2 chi square distribution, xp,k+1, yp,k+1Respectively the k+1 moment is
Overall view measures zk+1Coordinate components on the direction x and the direction y,WithRespectively particle i predicts observation at the k moment
AmountComponent on the direction x and the direction y,Respectively k+1 moment particle is observed on the direction x and the direction y
Covariance matrix；
Step 3) screens it according to the weight of particle, then carries out resampling to it；
Particle after step 4) fusion screening obtains the current state estimation of mobile robot, repeats step 1) -4),
To realize the mobile robot tracking under UHF-RFID environment.
2. being directed to more hypothesis UKF method for tracking target of UHF-RFID system as described in claim 1, it is characterised in that: institute
It states in step 1), the region where initial position is expressed as Ω, and M particle is then uniformly chosen in Ω, gives these particles
Original state isWherein It is randomly generated, these particle states is made
For the original state of mobile robot, initial given state variable is two dimension or multi-Dimensional parameters.
3. being directed to more hypothesis UKF method for tracking target of UHF-RFID system as claimed in claim 1 or 2, it is characterised in that:
In the step 3), when particle weightCast out the particle；Conversely, then claiming the particle effectively and retaining, number of effective particles
It is denoted as nc；Resampling will be carried out to particle simultaneously, as number of effective particles nc< η retainsParticle, andVariance is σ2Resampling is carried out in range, and number of effective particles is made to remain at η；Conversely, then skipping resampling
Stage.
4. being directed to more hypothesis UKF method for tracking target of UHF-RFID system as claimed in claim 1 or 2, it is characterised in that:
In the step 4), the particle after screening, resampling is normalized, merges and sieves by using average weighted method
Particle after choosing, to obtain mobile robot state estimation.
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| CN109870716B (en) * | 2017-12-01 | 2022-02-01 | 北京京东乾石科技有限公司 | Positioning method, positioning device and computer readable storage medium |
| CN109159112B (en) * | 2018-07-09 | 2022-03-29 | 天津大学 | Robot motion parameter estimation method based on unscented Kalman filtering |
| CN110225458B (en) * | 2019-01-14 | 2020-10-09 | 北京理工大学 | UWB positioning system and method based on hybrid filtering |
| CN110263905B (en) * | 2019-05-31 | 2021-03-02 | 上海电力学院 | Robot positioning and mapping method and device based on firefly optimized particle filtering |
| CN110296706A (en) * | 2019-07-11 | 2019-10-01 | 北京云迹科技有限公司 | Localization method and device, robot for Indoor Robot |
| CN112613222B (en) * | 2021-01-04 | 2023-09-15 | 重庆邮电大学 | Method for short-term prediction of ionized layer MUF by tilt detection based on improved particle filtering |
| CN113688258A (en) * | 2021-08-20 | 2021-11-23 | 广东工业大学 | Information recommendation method and system based on flexible multidimensional clustering |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101644758A (en) * | 2009-02-24 | 2010-02-10 | 中国科学院声学研究所 | Target localization and tracking system and method |
| CN102103815A (en) * | 2009-12-17 | 2011-06-22 | 上海电机学院 | Method and device for positioning particles of mobile robot |
| CN103206954A (en) * | 2013-04-25 | 2013-07-17 | 佳木斯大学 | Multi-sensor information fusion method for mobile robot based on UKF (Unscented Kalman Filter) |
| CN103529424A (en) * | 2013-10-23 | 2014-01-22 | 北京工商大学 | RFID (radio frequency identification) and UKF (unscented Kalman filter) based method for rapidly tracking indoor target |
-
2017
- 2017-01-12 CN CN201710021764.2A patent/CN106908762B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101644758A (en) * | 2009-02-24 | 2010-02-10 | 中国科学院声学研究所 | Target localization and tracking system and method |
| CN102103815A (en) * | 2009-12-17 | 2011-06-22 | 上海电机学院 | Method and device for positioning particles of mobile robot |
| CN103206954A (en) * | 2013-04-25 | 2013-07-17 | 佳木斯大学 | Multi-sensor information fusion method for mobile robot based on UKF (Unscented Kalman Filter) |
| CN103529424A (en) * | 2013-10-23 | 2014-01-22 | 北京工商大学 | RFID (radio frequency identification) and UKF (unscented Kalman filter) based method for rapidly tracking indoor target |
Non-Patent Citations (2)
| Title |
|---|
| Localization and Map-building of Mobile Robot Based on RFID Sensor Fusion System;Byoung-Suk Choi 等;《The IEEE International Conference on Industrial Informatics(INDIN 2008)》;20080716;第412-417页 * |
| 基于RFID技术与航迹推测的移动机器人融合定位方法和路径规划;王勤;《中国优秀硕士学位论文全文数据库信息科技辑》;20161015;正文第27页 * |
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