CN114488239A - Close combination robust relative position sensing method for vehicle collaborative navigation - Google Patents
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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
- 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
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
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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
- 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
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- 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
- G01S19/40—Correcting position, velocity or attitude
- G01S19/41—Differential correction, e.g. DGPS [differential GPS]
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Abstract
The invention discloses a close combination robust relative position sensing method for vehicle cooperative navigation, which relates to the technical field of position sensing, introduces a robust theory from the angle of data fusion estimation of a multi-navigation system, and further improves the sensing precision of relative positions by improving a data fusion means in a vehicle cooperative positioning solution. Aiming at the colored noise interference of a system and the precision loss caused by nonlinearity, a Robust volume filtering (RCKF) algorithm based on Huber M estimation is provided, and an Robust difference theory is integrated; firstly, time updating and measurement estimation are carried out through a three-order volume rule, so that the precision loss of a nonlinear system is avoided; and secondly, measuring, updating and controlling colored noise interference by using Huber M estimation to obtain system state estimation, fusing L1 and L2 norms, and reducing the weight of noise interference so as to improve the quality of the posterior information of the system. A new state quality control strategy is provided for VANET relative position perception.
Description
Technical Field
The invention relates to the field of position perception, in particular to a method for perceiving a relative position of a vehicle collaborative navigation tight combination robust.
Background
Relative location awareness as a prerequisite for co-location is based on location services and the basis of vehicle intelligent traffic [1-2 ]. A Global Navigation Satellite System (GNSS) is a preferred Navigation method for implementing relative position sensing and performing cooperative positioning due to its advantages of wide working range and mature technology. However, in urban environments, the compactness and height of buildings can result in reduced accuracy of relative position solutions that rely on GNSS. The integration of multiple satellite navigation systems can relieve the problem of urban canyons to a certain extent, but additionally adds interference errors among the multiple systems; meanwhile, the increased signal bandwidth of the receiver for realizing multi-system fusion inevitably absorbs more noise, and the cost and the power consumption of the receiver are also increased.
In order to avoid the problems and improve the cooperative positioning precision, researchers start with other navigation modes, introduce new observed quantities and put forward a new relative positioning solution;
XU B, SHEN L, YAN F,2009.Vehicular Node Positioning Based on Doppler-Shifted Frequency Measurement on high way [ J ]. Journal of Electronics (China),26(2): 265. 269. it is proposed to use the Doppler Measurement of the vehicle itself to obtain the relative position, and experiments indicate that the performance of the method in terms of precision and robustness is better than the resolving result Based on the Global Positioning System (GPS), but the implementation needs to place the radiation Node at the roadside, depending on the basic equipment;
ALAM N, TABATABAEI BALAEI A, DEMPSTER A G,2011.A DSRC Doppler-Based Cooperative position Enhancement for Vehicular Networks With GPS Availability [ J ]. IEEE Transactions on Vehicular Technology,60(9): 4462-4470. combining the position and speed of the adjacent vehicle, using Dedicated Short Range Communication (DSRC) to obtain Doppler frequency shift and further to propose a loose combination method, avoiding dependence on the infrastructure, and improving the performance by 48% compared With the independent GPS result;
ALAM N, TABATABAEI BALAEI A, DEMPSTER A G,2013, Relative Positioning Enhancement in VANETs, A light Integration application [ J ]. IEEE Transactions on adaptive transmission Systems,14(1), 47-55. adopt the double difference method of GPS bottom layer data for eliminating the space correlation error design, eliminate the errors such as satellite clock and ionized layer interference through carrying on the second difference to the pseudo-range, have reached higher Positioning performance to the Relative Differential GPS (Differential GPS, DGPS);
ALAM N, KEALY A, DEMPSTER A G,2013.An INS-Aided Navigation Integration for Relative position Enhancement in VANETs [ J ]. IEEE Transactions on Intelligent Transportation Systems,14(4): 1992-INS 1996. An Inertial Navigation System (INS) is introduced to further measure information comprehensively, and a cooperative Positioning solution is obtained by combining GPS bottom layer data and Relative acceleration between vehicles, but the algorithm only uses the INS to provide Relative acceleration, and the acquisition of the Euler angle of the System still depends on Doppler translation and cannot fully utilize INS information;
feng S, Joon W C, Andrew G D2015, An Ultra-Wide Band-based range/GPS tilt integration approach for relative positioning in vehicle ad hoc networks [ EB/OL ] (2015) [ 2021-09-10 ], summarizing the previous research, using Ultra Wide Band (UWB) with high communication quantity and strong anti-interference capability to measure distance of a workshop in a vehicle ad hoc network, simultaneously using INS to obtain An internal Euler angle of a system, and fusing a pseudo range double difference, a Doppler frequency shift double difference and a UWB distance to provide a close-combination cooperative positioning method, thereby obtaining a better positioning effect compared with the algorithms.
Although the relative position solution achieves higher precision by updating the measurement and combining with a new positioning mode, the data fusion means adopted by the algorithm is Extended Kalman Filter (EKF). The estimation method for directly performing Taylor expansion truncation approximation on the Gaussian integral can only reach first-order precision, and the estimation precision is further reduced in consideration of the nonlinearity of the system and the non-Gaussian interference of colored noise in practice, so that the performance of the collaborative navigation algorithm is not fully exerted.
The relative position perception is used as a core technology of cooperative navigation, is also a key of vehicle intelligent traffic, and plays an important role in a vehicle Ad Hoc network (VANET) cooperative positioning algorithm; however, due to the non-linearity and colored noise interference of the system, the acquisition of the a posteriori information of the co-location is usually limited to a certain precision under the same hardware condition; although the existing research improves the positioning accuracy by researching and exploring in the aspects of navigation modes and observation types, no effective solution is provided for the problems of accuracy loss caused by nonlinearity and pollution of colored noise to the observation.
Disclosure of Invention
In order to solve the defects of the prior art, a close-combination robust relative position sensing method for vehicle cooperative navigation is provided, a robust theory is introduced from the angle of data fusion estimation of a multi-navigation system, and the sensing precision of a relative position is further improved by improving a data fusion means in a vehicle cooperative positioning solution; aiming at the colored noise interference and the precision loss caused by nonlinearity of the system, a Robust volume filtering (RCKF) algorithm based on Huber M estimation is provided, and an anti-difference theory is integrated; time updating and measurement estimation are carried out through a third-order volume rule, so that the precision loss of a nonlinear system is avoided; measuring, updating and controlling colored noise interference by using Huber M estimation to obtain system state estimation, fusing L1 and L2 norms, and reducing the weight of noise interference so as to improve the quality of system posterior information; a new state quality control strategy is provided for the VANET relative position perception;
the technical scheme adopted by the invention is as follows:
a close combination robust relative position sensing method for vehicle collaborative navigation comprises the following steps;
s1, respectively equipping GPS receivers on the vehicle a and the vehicle b, respectively equipping UWB transceivers on the two vehicles at the same time, turning on the GPS receivers and the UWB transceivers of the vehicle a and the vehicle b, and starting and driving the vehicles;
s2, acquiring satellite pseudo-ranges and Doppler frequency shift quantities of vehicles a and b by using a GPS receiver, and acquiring the relative distance between the vehicles by using a UWB transceiver;
s3: the difference value of the reference satellite reference station coordinates and the satellite position is combined with the GPS pseudo range received by the satellite reference station for difference, the correction value of the GPS signal is obtained, and the GPS signals of the vehicles a and b are corrected;
s4: calculating the double difference of the GPS pseudo range and the Doppler frequency shift of an observation satellite;
the satellite pseudo range and the Doppler frequency shift quantity single difference obtained by the satellite to the satellite pseudo range and the Doppler frequency shift quantity of the vehicles a and b are subtracted from the satellite pseudo range and the Doppler frequency shift quantity single difference of the adjacent satellite to the vehicles a and b;
s5: performing single-point positioning by using the GPS signal corrected in S3 to obtain initial position coordinates of two vehicles, and performing backward intersection by using pseudo ranges of at least four satellites to obtain position coordinates of the vehicles;
s6: based on a Robust volume filtering (RCKF) algorithm of Huber M estimation, the initial relative positions of the two vehicles are obtained by using the initial position coordinates obtained in S5, and data fusion resolving is performed by combining the relative distance measured by the UWB transceiver, the double difference of doppler frequency shift measured by the GPS receiver, and the double difference of GPS pseudorange: calculating a volume point set at an initial moment, and setting a covariance matrix; secondly, at a second moment, performing state updating and measurement updating by using a volume point set, a relative position and a covariance matrix at an initial moment and combining measurement of a UWB transceiver and a GPS receiver at the current moment to obtain the relative position and the covariance matrix at the current moment, then performing iterative updating by using the relative position and the covariance matrix at the previous moment at a next moment, and repeating the state and measurement updating process to obtain a posterior relative position at the next moment, wherein the method specifically comprises the following steps:
s6.1: calculating a volume point set according to the initial time of S5:
generating a volume point set with equal weight at the initial moment according to the Cubasic rule, obtaining the relative position at the initial moment by using the difference between the initial positions of the two vehicles obtained by S5, setting the relative speed to be zero and setting a covariance matrix;
s6.2: and (3) time updating: from the second moment, simulating the state transition probability of the system by utilizing the volume point set and the covariance matrix at the previous moment and the relative position and speed combined with a third-order volume rule to obtain a prior three-dimensional relative position, a prior three-dimensional relative speed and a prior covariance matrix for estimating a posterior position and speed;
s6.3: and (3) carrying out measurement updating:
updating the volume point set according to the prior covariance matrix and the third-order volume rule again;
carrying out weighting fusion estimation on the measured volume points by using the new volume points, and obtaining the measured estimation values of each sensor at the current moment, namely satellite pseudo-range double differences, Doppler frequency shift double differences and estimation values of the relative distance of a UWB transceiver;
measuring the estimated value and the prior three-dimensional relative position by using the current moment quantity and the quantity, and combining the relative speed with an observation equation to construct a linear regression;
solving the linear regression by using Huber M estimation to obtain a three-dimensional relative position and relative speed of the posterior at the current moment and a covariance matrix thereof;
s6.4, repeating S6.2 and S6.3 at the next moment, and iteratively updating time and measurement;
s7: and (4) iteratively executing time updating and measurement updating in the step S6 along with the running time of the vehicle until the vehicle stops running, and shutting down each sensor to finally obtain the posterior state estimation and the covariance matrix of the posterior state estimation.
The a posteriori state estimates include three-dimensional relative positions and three-dimensional relative velocities.
Advantageous technical effects
The invention has proposed the relative position perception method of tight combination robust of a vehicle collaborative navigation, EKF and classical volume filtering (Cubaure Kalman Filter, CKF) precision in the background art are equivalent, and RCKF carries on the nonlinear system transmission because of using the volume rule, incorporate robust M to estimate and carry on the interference suppression to colored noise in the actual measurement at the same time, therefore, the performance on precision and robustness is superior to EKF and CKF; the relative position perception accuracy can be further improved by improving the data fusion method, and a system quality control strategy with practical significance is provided for a vehicle cooperative positioning scheme.
Drawings
FIG. 1 is a flow chart of a method for sensing a relative position of a tightly combined robust for collaborative navigation of a vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of UWB communication and ranging provided by an embodiment of the present invention;
fig. 3 is a flow chart of the RCKF algorithm provided by the embodiment of the present invention.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings and examples;
the invention provides a close combination robust relative position sensing method for vehicle cooperative navigation, which introduces a robust theory from the angle of data fusion estimation of a multi-navigation system, and further improves the sensing precision of relative positions by improving a data fusion means in a vehicle cooperative positioning solution; aiming at the colored noise interference and the precision loss caused by nonlinearity of the system, a Robust volume Filter (RCKF) based on Huber M estimation is provided, and the algorithm is integrated into an anti-difference theory; firstly, time updating and measurement estimation are carried out through a third-order volume rule, so that the precision loss of a nonlinear system is avoided; secondly, measuring, updating and controlling colored noise interference by using Huber M estimation to obtain system state estimation, fusing L1 and L2 norms, and reducing the weight of noise interference so as to improve the quality of the posterior information of the system; a new state quality control strategy is provided for the VANET relative position perception;
the technical scheme adopted by the invention is as follows:
a method for sensing a close-combination robust relative position of vehicle cooperative navigation is shown in FIG. 1 and comprises the following steps;
s1: respectively arranging GPS receivers on the vehicle a and the vehicle b, as shown in FIG. 2, simultaneously respectively arranging two UWB transceivers on the two vehicles, turning on the GPS receivers and the UWB transceivers of the vehicle a and the vehicle b, and starting and running the vehicles;
in the embodiment, a come card GS10 GPS receiver Novatel INS-LCI (integrated GNSS-INS) is respectively arranged on a vehicle a and a vehicle b, the high-precision positions of the vehicle a and the vehicle b based on a carrier phase difference (Real Time Kinematic, RTK) are obtained and used as Real reference values to calculate position errors, two UWB transceivers are respectively arranged on the two vehicles at the same Time, a satellite receiver and the UWB transceiver of the vehicle a and the vehicle b are opened, and the vehicles are started to run in parallel;
s2, acquiring satellite pseudo ranges and Doppler frequency shift quantities of vehicles a and b by using a GPS receiver, and acquiring a relative distance between the two vehicles by using a UWB transceiver;
s3: the difference value of the reference satellite reference station coordinates and the satellite position is combined with the GPS pseudo range received by the satellite reference station for difference, the correction value of the GPS signal is obtained, and the GPS signals of the vehicles a and b are corrected;
s4: calculating the double difference of the GPS pseudo range and the Doppler frequency shift of an observation satellite;
the pseudo range/Doppler frequency shift single difference obtained by the satellite subtracting the pseudo range/Doppler frequency shift of the vehicles a and b is subtracted from the pseudo range/Doppler frequency shift single difference of the adjacent satellite subtracting the vehicles a and b;
s5: performing single-point positioning by using the GPS signal corrected in S3 to obtain initial position coordinates of two vehicles, and performing backward intersection by using pseudo ranges of at least four satellites to obtain position coordinates of the vehicles;
s6: based on a Robust volume filtering (RCKF) algorithm estimated by Huber M, the initial position coordinates of the two vehicles obtained by S5 are used to obtain an initial relative position, as shown in fig. 3, data fusion calculation is performed by combining the relative distance measured by the UWB transceiver, the double difference of doppler frequency shift measured by the GPS receiver, and the double difference of GPS pseudorange: calculating a volume point set at an initial moment, and setting a covariance matrix; secondly, at a second moment, performing state updating and measurement updating by using a volume point set, a relative position and a covariance matrix at an initial moment and combining a UWB transceiver and a GPS receiver measurement at the current moment to obtain the relative position and the covariance matrix at the current moment, then performing iterative updating by using the relative position and the covariance matrix at the previous moment at the next moment, and repeating the state and measurement updating process to obtain the posterior relative position at the next moment; the specific process is as follows:
s6.1: calculating a volume point set according to the initial time of S5:
generating a volume point set with equal weight at the initial moment according to the Cubasic rule, obtaining the relative position at the initial moment by using the difference between the initial positions of the two vehicles obtained by S5, setting the relative speed to be zero, and setting a covariance matrix according to experience;
s6.2: and (3) time updating: from the second moment, simulating the state transition probability of the system by utilizing the volume point set and the covariance matrix at the previous moment and the relative position and speed combined with a third-order volume rule to obtain a prior three-dimensional relative position, a prior three-dimensional relative speed and a prior covariance matrix for estimating a posterior position and speed;
s6.3: and (3) measurement updating:
updating the volume point set according to the prior covariance matrix and the third-order volume rule again;
carrying out weighting fusion estimation on the measured volume points by using the new volume points, and obtaining the measured estimation values of each sensor at the current moment, namely satellite pseudo-range double differences, Doppler frequency shift double differences and estimation values of the relative distance of a UWB transceiver;
measuring the estimated value and the prior three-dimensional relative position by using the current moment quantity and the quantity, and combining the relative speed with an observation equation to construct a linear regression;
solving the linear regression by using Huber M estimation to obtain a three-dimensional relative position and relative speed of the posterior at the current moment and a covariance matrix thereof;
s6.4, repeating S6.2 and S6.3 at the next moment, and iteratively updating time and measurement;
s7: and (4) iteratively executing time updating and measurement updating in the step S6 along with the running time of the vehicle until the vehicle stops running, and shutting down each sensor to finally obtain the posterior state estimation and the covariance matrix of the posterior state estimation.
The a posteriori state estimates include three-dimensional relative positions and three-dimensional relative velocities.
To verify the authenticity of the experiment, as shown in table 1, under the same experimental conditions, Robust volume Filtering (RCKF) versus Extended Kalman Filtering (EKF) is improved in terms of Root Mean Square (RMS), Accuracy (Accuracy), robustness (Precision);
TABLE 1
Claims (4)
1.A close combination robust relative position sensing method for vehicle collaborative navigation is characterized in that: comprises the following steps;
s1, respectively arranging GPS receivers on a vehicle a and a vehicle b, simultaneously respectively arranging UWB transceivers on the two vehicles, turning on the GPS receivers and the UWB transceivers of the vehicle a and the vehicle b, and starting and driving the vehicles;
s2, acquiring satellite pseudo ranges and Doppler frequency shift quantities of the vehicles a and b by using a GPS receiver, and acquiring a relative distance between the vehicles by using a UWB transceiver;
s3: the difference value of the reference satellite reference station coordinates and the satellite position is combined with the GPS pseudo range received by the satellite reference station for difference, the correction value of the GPS signal is obtained, and the GPS signals of the vehicles a and b are corrected;
s4: calculating the double difference of the GPS pseudo range and the Doppler frequency shift of an observation satellite;
s5: performing single-point positioning by using the GPS signal corrected in S3 to obtain initial position coordinates of two vehicles, and performing backward intersection by using pseudo ranges of at least four satellites to obtain position coordinates of the vehicles;
s6: based on RCKF algorithm of Huber M estimation, utilize the initial position coordinate of two cars that S5 obtained to obtain initial relative position, combine the relative distance that UWB transceiver measured, Doppler shift double difference and GPS pseudo range double difference that GPS receiver measured to carry on the data fusion to solve: calculating a volume point set at an initial moment, and setting a covariance matrix; secondly, at a second moment, performing state updating and measurement updating by using a volume point set, a relative position and a covariance matrix at an initial moment and combining a UWB transceiver and a GPS receiver measurement at the current moment to obtain the relative position and the covariance matrix at the current moment, then performing iterative updating by using the relative position and the covariance matrix at the previous moment at the next moment, and repeating the state and measurement updating process to obtain the posterior relative position at the next moment;
s7: and (4) iteratively executing time updating and measurement updating in the step S6 along with the running time of the vehicle until the vehicle stops running, and shutting down each sensor to finally obtain the posterior state estimation and the covariance matrix of the posterior state estimation.
2. The method for close-coupled robust relative position sensing for vehicle collaborative navigation according to claim 1, wherein: the specific process of S6 is as follows: and the satellite pseudo range and the Doppler frequency shift single difference obtained by the satellite to the satellite pseudo range and the Doppler frequency shift of the vehicles a and b are subtracted from the satellite pseudo range and the Doppler frequency shift single difference of the adjacent satellite to the vehicles a and b.
3. The method for close-coupled robust relative position sensing for vehicle collaborative navigation according to claim 1, wherein: the specific process of S6 includes the following steps:
s6.1: calculating a volume point set according to the initial time of S5:
generating a volume point set with equal weight at the initial moment according to the Cubasic rule, obtaining the relative position at the initial moment by using the difference between the initial positions of the two vehicles obtained by S5, setting the relative speed to be zero and setting a covariance matrix;
s6.2: and (3) time updating: from the second moment, simulating the state transition probability of the system by utilizing the volume point set and the covariance matrix at the previous moment and the relative position and speed combined with a third-order volume rule to obtain a prior three-dimensional relative position, a prior three-dimensional relative speed and a prior covariance matrix for estimating a posterior position and speed;
s6.3: and (3) carrying out measurement updating:
updating the volume point set according to the prior covariance matrix and the third-order volume rule again;
carrying out weighting fusion estimation on the measured volume points by using the new volume points, and obtaining the measured estimation values of each sensor at the current moment, namely satellite pseudo-range double differences, Doppler frequency shift double differences and estimation values of the relative distance of a UWB transceiver;
measuring the estimated value and the prior three-dimensional relative position by using the current moment quantity and the quantity, and combining the relative speed with an observation equation to construct a linear regression;
solving the linear regression by using Huber M estimation to obtain a three-dimensional relative position and relative speed of the posterior at the current moment and a covariance matrix thereof;
s6.4 repeats S6.2 and S6.3 at the next time, iteratively performing the time update and the measurement update.
4. The method for close-coupled robust relative position sensing for vehicle collaborative navigation according to claim 1, wherein: the a posteriori state estimates include three-dimensional relative positions and three-dimensional relative velocities.
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