CN107274721B - Multi-vehicle cooperative positioning method in intelligent transportation system - Google Patents

Multi-vehicle cooperative positioning method in intelligent transportation system Download PDF

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CN107274721B
CN107274721B CN201710423028.XA CN201710423028A CN107274721B CN 107274721 B CN107274721 B CN 107274721B CN 201710423028 A CN201710423028 A CN 201710423028A CN 107274721 B CN107274721 B CN 107274721B
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杜利平
侯晓田
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University of Science and Technology Beijing USTB
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Abstract

The invention provides a multi-vehicle cooperative positioning method in an intelligent traffic system, which can improve the positioning accuracy of vehicles. The method comprises the following steps: calculating a position measurement value of the target vehicle at the next moment according to the obtained position observation value and the obtained motion observation value of the target vehicle at the current moment; acquiring the number of the adjacent vehicles, and constructing a motion equation of the system by combining the acquired position observation value and motion observation value of the target vehicle at the current moment and the calculated position measurement value of the target vehicle at the next moment; calculating relative position information between the target vehicle and the adjacent vehicle according to the acquired position observation values of the target vehicle and the adjacent vehicle at the current moment, and constructing an observation equation of the system according to the number of the adjacent vehicles; and substituting the motion equation and the observation equation of the system into the extended Kalman filtering to obtain the position estimation value of the target vehicle. The invention relates to the technical field of wireless positioning.

Description

Multi-vehicle cooperative positioning method in intelligent transportation system
Technical Field
The invention relates to the technical field of wireless positioning, in particular to a multi-vehicle cooperative positioning method in an intelligent traffic system.
Background
In recent years, as vehicles become more intelligent and automated, in an intelligent transportation system, various safety-related applications, such as real-time estimation of traffic conditions, a collision warning system, a lane departure warning system, and the like, are used to improve the efficiency and safety of driving, thereby reducing vehicle collision accidents. These security applications rely primarily on vehicle location information provided by the local transportation network. Vehicle navigation technologies include the Global Positioning System (GPS), global satellite navigation system (GLONASS), galileo and Beidou systems (BDS), which can provide location information to a vehicle user. GPS is one of the most common positioning devices in vehicle positioning. However, it is well known that GPS signals are subject to different sources of noise and degradation and temporary loss of signal in complex environments, and GPS satellite visibility estimates are inadequate, which makes GPS unable to provide accurate location information in all situations. Our low cost GPS receiver navigation systems for automotive applications suffer from low accuracy and frequent signal interruption problems. Typically, the accuracy of a GPS is nominally about 10m, which is too high for a vehicle active safety system.
One of the most common methods for improving the accuracy of self-positioning is to use other embedded information sources, combine navigation data, and obtain a more accurate position estimation through data fusion. The current technology for improving GPS performance is based on Kalman filtering (Kalman filtering). The main idea of the method based on Kalman filtering is to reduce GPS pseudo-range errors through filtering, but the method does not combine position information of surrounding vehicles, and the positioning accuracy provided in an intelligent transportation system is not high.
Disclosure of Invention
The invention aims to provide a multi-vehicle cooperative positioning method in an intelligent traffic system, so as to solve the problem of low positioning accuracy in the prior art.
In order to solve the above technical problem, an embodiment of the present invention provides a method for cooperatively locating multiple vehicles in an intelligent transportation system, including:
obtaining position observation values of a target vehicle and an adjacent vehicle in a system at the current moment, wherein the adjacent vehicle is a vehicle adjacent to the target vehicle;
obtaining a motion observation value of the target vehicle at the current moment;
calculating a position measurement value of the target vehicle at the next moment according to the obtained position observation value and the obtained motion observation value of the target vehicle at the current moment;
acquiring the number of the adjacent vehicles, and constructing a motion equation of the system by combining the acquired position observation value and motion observation value of the target vehicle at the current moment and the calculated position measurement value of the target vehicle at the next moment;
calculating relative position information between the target vehicle and the adjacent vehicle according to the acquired position observation values of the target vehicle and the adjacent vehicle at the current moment, and constructing an observation equation of the system according to the number of the adjacent vehicles;
and substituting the motion equation and the observation equation of the system into the extended Kalman filtering to obtain the position estimation value of the target vehicle.
Further, the obtaining of the position observations of the target vehicle and the neighboring vehicle at the current time in the system comprises:
obtaining a target vehicle X at time k0Is observed at a position of X0k＝[x0ky0kθ0k]T；
Where k denotes the current time, T denotes the transpose, and x0kIndicating target vehicle X at time k0In the x-axis, y0kIndicating target vehicle X at time k0In the y-axis coordinate, θ0kIndicating target vehicle X at time k0The included angle formed by the motion direction and the x axis;
obtaining adjacent vehicles X at time kjIs observed at a position of Xjk＝[xjkyjkθjk]T，(j＝1,2…N)；
Wherein N represents the number of adjacent vehicles, xjkIndicating the adjacent vehicle X at time kjIn the x-axis, yjkIndicating the adjacent vehicle X at time kjIn the y-axis coordinate, θjkIndicating the adjacent vehicle X at time kjThe direction of motion forms an angle with the x-axis.
Further, the obtaining of the motion observation value of the target vehicle at the current time includes:
obtaining a target vehicle X at time k0Is observed in the movement u0k＝[V0ka0kφ0k]T；
Wherein, V0kIndicating target vehicle X at time k0A velocity of0kIndicating target vehicle X at time k0Acceleration of phi0kIndicating target vehicle X at time k0The steering angle of (c).
Further, the calculating a position measurement value of the target vehicle at the next time according to the obtained position observation value and the obtained motion observation value of the target vehicle at the current time comprises:
according to the target vehicle X0Position observation X at time k0kAnd the motion observation u0kAnd a target vehicle X0Calculating the target vehicle X0Position measurement X at time k +10(k+1)＝f(X0k,u0k) Wherein, f (X)0k,u0k) Indicating target vehicle X0Discrete equations of motion of the motion model of (a);
according to f (X)0k,u0k) Obtaining f (X)0k,u0k) Observed value X with respect to position0kOf the jacobian matrix
Figure BDA0001315493600000037
Comprises the following steps:
Figure BDA0001315493600000031
according to f (X)0k,u0k) Obtaining f (X)0k,u0k) Observed value u about movement0kOf the Jacobian matrix Bu0kComprises the following steps:
Figure BDA0001315493600000032
further, the acquiring the number of the adjacent vehicles, and combining the acquired position observation value and the acquired motion observation value of the target vehicle at the current moment, and the calculated position measurement value of the target vehicle at the next moment, the constructing a system motion equation comprises:
acquiring the number N of the adjacent vehicles;
combining the acquired position observed value X of the target vehicle at the time k according to the acquired number N of the adjacent vehicles0kAnd the motion observation u0kConstructing a system state of the entire system including the target vehicle and the neighboring vehicle at the time k
Figure BDA0001315493600000033
System input
Figure BDA0001315493600000034
The equation of motion of the system is Xk+1＝f(Xk,uk) (ii) a Wherein T represents a matrix transpose;
according to the Jacobian matrix
Figure BDA0001315493600000035
Obtaining the motion equation f (X) of the systemk,uk) About system stateState XkJacobian matrix akComprises the following steps:
Figure BDA0001315493600000036
according to the Jacobian matrix
Figure BDA0001315493600000041
Obtaining the motion equation f (X) of the systemk,uk) About system input ukOf the Jacobian matrix BkComprises the following steps:
Figure BDA0001315493600000042
further, the calculating the relative position information between the target vehicle and the adjacent vehicle according to the obtained position observation values of the target vehicle and the adjacent vehicle at the current moment, and constructing the observation equation of the system according to the number of the adjacent vehicles comprises:
according to the acquired k time, the target vehicle X0Is observed at a position of X0kAnd adjacent vehicle XjIs observed at a position of XjkCalculating the target vehicle X at the time k0And adjacent vehicle XjRelative position information Z therebetweenjk；
According to the calculated k time target vehicle X0And adjacent vehicle XjRelative position information Z therebetweenjkConstructing the observation equation Z of the systemk＝[Z1kZ2k… Zjk… Z(N-1)kZNk]T(j ═ 1,2, … N), where N represents the number of adjacent vehicles; wherein, Z isjkExpressed as:
Figure BDA0001315493600000043
wherein h isj(. h) a calculation formula representing relative positional information, djkIs a target vehicle X0With adjacent vehicle XjThe relative distance between the two or more of them,
Figure BDA0001315493600000044
is a target vehicle X0With adjacent vehicle XjRelative azimuth angle therebetween;
according to Zjk＝hj(X0k,Xjk) Target vehicle X0Relative to Zjk＝hj(X0k,Xjk) Of the jacobian matrix HjkComprises the following steps:
Figure BDA0001315493600000045
according to the Jacobian matrix HjkTo obtain an observation equation ZkOf the jacobian matrix HkComprises the following steps:
Figure BDA0001315493600000051
further, the step of substituting the motion equation and the observation equation of the system into the extended kalman filter to obtain the position estimation value of the target vehicle includes:
will system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkObservation equation ZkOf the jacobian matrix HkAnd equation of motion X of the systemk+1And observation equation ZkSubstituting the target vehicle X into the extended Kalman filtering to obtain the target vehicle X0Position estimate of
Figure BDA0001315493600000052
Further, the step of substituting the motion equation and the observation equation of the system into the extended kalman filter to obtain the position estimation value of the target vehicle includes:
s1, according to the system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkAnd calculating the predicted value of the covariance of the system state at the moment of k +1
Figure BDA0001315493600000053
Wherein, PkThe covariance of the system state at time k, Q the covariance of the error of the position observation, T the matrix transposition, TsIs a sampling period;
s2, according to the observation equation ZkOf the jacobian matrix HkAnd the predicted value of the covariance of the system state at the moment of k +1 is obtained through calculation
Figure BDA0001315493600000054
Computing k +1 time Kalman filter gain
Figure BDA0001315493600000055
Wherein R is an observation equation ZkError covariance of (2);
s3, obtaining a Kalman filtering gain K according to the K +1 moment obtained by calculationk+1And calculating the covariance of the system state at the moment k +1
Figure BDA0001315493600000056
Wherein I is an identity matrix;
s4, according to the system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkCalculating the predicted value of the system state at the moment k +1
Figure BDA0001315493600000057
And system observation equation prediction
Figure BDA0001315493600000058
Wherein,
the above-mentioned
Figure BDA0001315493600000061
And
Figure BDA0001315493600000062
respectively expressed as:
Figure BDA0001315493600000063
wherein,
Figure BDA0001315493600000064
n represents the number of adjacent vehicles;
Figure BDA0001315493600000065
wherein,
Figure BDA0001315493600000066
hj(. -) a calculation formula representing relative position information;
s5, according to the obtained system observation equation predicted value
Figure BDA0001315493600000067
Determination of target vehicle X0With adjacent vehicle XjRelative azimuth angle predicted value therebetween
Figure BDA0001315493600000068
Whether a preset judgment formula is met or not, and if so, determining the estimated value of the system state at the k +1 moment
Figure BDA0001315493600000069
If not, determining the estimated value of the system state at the k +1 moment
Figure BDA00013154936000000610
Comprises the following steps:
Figure BDA00013154936000000611
wherein,
Figure BDA00013154936000000612
Zk+1the observed value of the system observation equation at the moment k +1 is obtained;
s6, estimating the system state according to the k +1 time
Figure BDA00013154936000000613
ComputingTarget vehicle position estimation value at k +1 moment
Figure BDA00013154936000000614
Comprises the following steps:
Figure BDA00013154936000000615
further, the preset judgment formula is expressed as:
Figure BDA00013154936000000616
wherein, thetathreshIs a preset threshold value.
Further, the
Figure BDA0001315493600000071
Wherein,
Figure BDA0001315493600000072
is a target vehicle X0With adjacent vehicle XjThe variance of the relative azimuth angle therebetween.
The technical scheme of the invention has the following beneficial effects:
according to the scheme, the position measurement value of the target vehicle at the next moment is calculated through the acquired position observation value and the acquired motion observation value of the target vehicle at the current moment; acquiring the number of the adjacent vehicles, and constructing a motion equation of the system by combining the acquired position observation value and motion observation value of the target vehicle at the current moment and the calculated position measurement value of the target vehicle at the next moment; calculating relative position information between the target vehicle and the adjacent vehicle according to the acquired position observation values of the target vehicle and the adjacent vehicle at the current moment, and constructing an observation equation of the system according to the number of the adjacent vehicles; substituting a motion equation and an observation equation of the system into the extended Kalman filtering to obtain a position estimation value of the target vehicle; therefore, the position observation value of the target vehicle is combined with the position observation value of the adjacent vehicle, the relative position information between the target vehicle and the adjacent vehicle is calculated, the position of the target vehicle is estimated after filtering is carried out by using the extended Kalman filtering based on the calculated relative position information between the target vehicle and the adjacent vehicle, the cooperative positioning is realized, the position error can be reduced, and the positioning precision of the vehicle is improved.
Drawings
Fig. 1 is a schematic flowchart of a multi-vehicle cooperative positioning method in an intelligent transportation system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a multi-vehicle system model of an intelligent transportation system according to an embodiment of the invention;
FIG. 3 is a schematic diagram illustrating a comparison between a measured position error and a GPS measured position error according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a multi-vehicle cooperative positioning method in an intelligent traffic system, aiming at the problem of low positioning accuracy in the prior art.
As shown in fig. 1, a method for cooperatively locating multiple vehicles in an intelligent transportation system according to an embodiment of the present invention includes:
s101, obtaining position observation values of a target vehicle and an adjacent vehicle in a system at the current moment, wherein the adjacent vehicle is a vehicle adjacent to the target vehicle;
s102, obtaining a motion observation value of the target vehicle at the current moment;
s103, calculating a position measurement value of the target vehicle at the next moment according to the acquired position observation value and the acquired motion observation value of the target vehicle at the current moment;
s104, acquiring the number of the adjacent vehicles, and constructing a motion equation of the system by combining the acquired position observation value and motion observation value of the target vehicle at the current moment and the calculated position measurement value of the target vehicle at the next moment;
s105, calculating relative position information between the target vehicle and the adjacent vehicle according to the acquired position observation values of the target vehicle and the adjacent vehicle at the current moment, and constructing an observation equation of the system according to the number of the adjacent vehicles;
and S106, substituting the motion equation and the observation equation of the system into the extended Kalman filtering to obtain the position estimation value of the target vehicle.
According to the multi-vehicle cooperative positioning method in the intelligent transportation system, the position measurement value of the target vehicle at the next moment is calculated through the acquired position observation value and the acquired motion observation value of the target vehicle at the current moment; acquiring the number of the adjacent vehicles, and constructing a motion equation of the system by combining the acquired position observation value and motion observation value of the target vehicle at the current moment and the calculated position measurement value of the target vehicle at the next moment; calculating relative position information between the target vehicle and the adjacent vehicle according to the acquired position observation values of the target vehicle and the adjacent vehicle at the current moment, and constructing an observation equation of the system according to the number of the adjacent vehicles; substituting a motion equation and an observation equation of the system into the extended Kalman filtering to obtain a position estimation value of the target vehicle; therefore, the position observation value of the target vehicle is combined with the position observation value of the adjacent vehicle, the relative position information between the target vehicle and the adjacent vehicle is calculated, the position of the target vehicle is estimated after filtering is carried out by using the extended Kalman filtering based on the calculated relative position information between the target vehicle and the adjacent vehicle, the cooperative positioning is realized, the position error can be reduced, and the positioning precision of the vehicle is improved.
According to the multi-vehicle cooperative positioning method in the intelligent transportation system, the measurement result is more accurate than the GPS measurement result in the low signal-to-noise ratio environment, the extended Kalman filtering operation speed is high, and the positioning precision of the vehicle is improved. In practical application, the number of adjacent vehicles can be adaptively selected according to practical situations. In the intelligent transportation system, adjacent vehicles around a target vehicle are constantly changed, and the method for cooperatively positioning the multiple vehicles in the intelligent transportation system can autonomously select the number of the adjacent vehicles around the target vehicle.
In an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, further, the obtaining position observations of a target vehicle and an adjacent vehicle at a current time in the system includes:
obtaining a target vehicle X at time k0Is observed at a position of X0k＝[x0ky0kθ0k]T；
Where k denotes the current time, T denotes the transpose, and x0kIndicating target vehicle X at time k0In the x-axis, y0kIndicating target vehicle X at time k0In the y-axis coordinate, θ0kIndicating target vehicle X at time k0The included angle formed by the motion direction and the x axis;
obtaining adjacent vehicles X at time kjIs observed at a position of Xjk＝[xjkyjkθjk]T，(j＝1,2…N)；
Wherein N represents the number of adjacent vehicles, xjkIndicating the adjacent vehicle X at time kjIn the x-axis, yjkIndicating the adjacent vehicle X at time kjIn the y-axis coordinate, θjkIndicating the adjacent vehicle X at time kjThe direction of motion forms an angle with the x-axis.
In the present embodiment, the target vehicle X0Target vehicle X at time k can be acquired through vehicle navigation (e.g., GPS device or Beidou device)0Is observed at a position of X0k＝[x0ky0kθ0k]T(ii) a At the same time, the target vehicle X0The k-time adjacent vehicle X may be acquired by Short-Range Communication (DSRC)jIs observed at a position of Xjk＝[xjkyjkθjk]T(j ═ 1,2 … N), the expenditure is small.
In the present embodiment, θkIndicating target vehicle X at time k0The angle formed by the direction of movement and the X-axis, i.e. the target vehicle X at time k0The azimuth of (d); thetajkIndicating the adjacent vehicle X at time kjThe angle formed by the direction of movement and the X-axis, i.e. the adjacent vehicle X at time kjIs measured.
In an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, further, the obtaining a motion observation value of the target vehicle at the current time includes:
obtaining a target vehicle X at time k0Is observed in the movement u0k＝[V0ka0kφ0k]T；
Wherein, V0kIndicating target vehicle X at time k0A velocity of0kIndicating target vehicle X at time k0Acceleration of phi0kIndicating target vehicle X at time k0The steering angle of (c).
In this embodiment, the target vehicle X at time k may be acquired by the vehicle-mounted sensor0Is observed in the movement u0k＝[V0ka0kφ0k]TWherein the onboard sensors may include, but are not limited to: acceleration sensor, speed sensor.
In an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, further, the calculating, according to the obtained position observation value and the obtained motion observation value of the target vehicle at the current time, a position measurement value of the target vehicle at a next time includes:
according to the target vehicle X0Position observation X at time k0kAnd the motion observation u0kAnd a target vehicle X0Calculating the target vehicle X0Position measurement X at time k +10(k+1)＝f(X0k,u0k) Wherein, f (X)0k,u0k) Indicating target vehicle X0Discrete equations of motion of the motion model of (a);
according to f (X)0k,u0k) Obtaining f (X)0k,u0k) Observed value X with respect to position0kOf the jacobian matrix
Figure BDA0001315493600000107
Comprises the following steps:
Figure BDA0001315493600000101
according to f (X)0k,u0k) Obtaining f (X)0k,u0k) Observed value u about movement0kOf the jacobian matrix
Figure BDA0001315493600000108
Comprises the following steps:
Figure BDA0001315493600000102
in an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, further, the acquiring the number of the neighboring vehicles, and combining the acquired position observed value and motion observed value of the target vehicle at the current time, and the calculated position measured value of the target vehicle at the next time, constructing a motion equation of the system includes:
acquiring the number N of the adjacent vehicles;
combining the acquired position observed value X of the target vehicle at the time k according to the acquired number N of the adjacent vehicles0kAnd the motion observation u0kConstructing a system state of the entire system including the target vehicle and the neighboring vehicle at the time k
Figure BDA0001315493600000103
System input
Figure BDA0001315493600000104
The equation of motion of the system is Xk+1＝f(Xk,uk) (ii) a Wherein T represents a matrix transpose;
according to the Jacobian matrix
Figure BDA0001315493600000105
Obtaining the motion equation f (X) of the systemk,uk) About System State XkJacobian matrix akComprises the following steps:
Figure BDA0001315493600000106
according to the Jacobian matrix
Figure BDA0001315493600000111
Obtaining the motion equation f (X) of the systemk,uk) About system input ukOf the Jacobian matrix BkComprises the following steps:
Figure BDA0001315493600000112
in an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, further, the calculating, according to the obtained position observed values of the target vehicle and the adjacent vehicles at the current time, relative position information between the target vehicle and the adjacent vehicles, and constructing an observation equation of the system according to the number of the adjacent vehicles includes:
according to the acquired k time, the target vehicle X0Is observed at a position of X0kAnd adjacent vehicle XjIs observed at a position of XjkCalculating the target vehicle X at the time k0And adjacent vehicle XjRelative position information Z therebetweenjk；
According to the calculated k time target vehicle X0And adjacent vehicle XjRelative position information Z therebetweenjkConstructing the observation equation Z of the systemk＝[Z1kZ2k… Zjk… Z(N-1)kZNk]T(j ═ 1,2, … N), where N represents the number of adjacent vehicles; wherein, Z isjkExpressed as:
Figure BDA0001315493600000113
wherein h isj(. h) a calculation formula representing relative positional information, djkIs a target vehicle X0With adjacent vehicle XjThe relative distance between the two or more of them,
Figure BDA0001315493600000114
is a target vehicle X0With adjacent vehicle XjRelative azimuth angle therebetween;
according to Zjk＝hj(X0k,Xjk) Target vehicle X0Relative to Zjk＝hj(X0k,Xjk) Of the jacobian matrix HjkComprises the following steps:
Figure BDA0001315493600000121
according to the Jacobian matrix HjkTo obtain an observation equation ZkOf the jacobian matrix HkComprises the following steps:
Figure BDA0001315493600000122
in an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, further, the substituting a motion equation and an observation equation of the system into the extended kalman filter to obtain the position estimation value of the target vehicle includes:
will system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkObservation equation ZkOf the jacobian matrix HkAnd equation of motion X of the systemk+1And observation equation ZkSubstituting the target vehicle X into the extended Kalman filtering to obtain the target vehicle X0Position estimate of
Figure BDA0001315493600000123
In an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, further, the substituting a motion equation and an observation equation of the system into the extended kalman filter to obtain the position estimation value of the target vehicle includes:
s1, according to the system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkAnd calculating the predicted value of the covariance of the system state at the moment of k +1
Figure BDA0001315493600000124
Wherein, PkThe covariance of the system state at time k, Q the covariance of the error of the position observation, T the matrix transposition, TsIs a sampling period;
s2, according to the observation equation ZkOf the jacobian matrix HkAnd the predicted value of the covariance of the system state at the moment of k +1 is obtained through calculation
Figure BDA0001315493600000125
Computing k +1 time Kalman filter gain
Figure BDA0001315493600000131
Wherein R is an observation equation ZkError covariance of (2);
s3, obtaining a Kalman filtering gain K according to the K +1 moment obtained by calculationk+1And calculating the covariance of the system state at the moment k +1
Figure BDA0001315493600000132
Wherein I is an identity matrix;
s4, according to the system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkCalculating the predicted value of the system state at the moment k +1
Figure BDA0001315493600000133
And system observation equation prediction
Figure BDA0001315493600000134
Wherein,
the above-mentioned
Figure BDA0001315493600000135
And
Figure BDA0001315493600000136
respectively expressed as:
Figure BDA0001315493600000137
wherein,
Figure BDA0001315493600000138
n represents the number of adjacent vehicles;
Figure BDA0001315493600000139
wherein,
Figure BDA00013154936000001310
hj(. -) a calculation formula representing relative position information;
s5, according to the obtained system observation equation predicted value
Figure BDA00013154936000001311
Determination of target vehicle X0With adjacent vehicle XjRelative azimuth angle predicted value therebetween
Figure BDA00013154936000001312
Whether a preset judgment formula is met or not, and if so, determining the estimated value of the system state at the k +1 moment
Figure BDA00013154936000001313
If not, determining the estimated value of the system state at the k +1 moment
Figure BDA00013154936000001314
Comprises the following steps:
Figure BDA00013154936000001315
wherein,
Figure BDA00013154936000001316
Zk+1observed value of system observation equation at k +1 moment；
S6, estimating the system state according to the k +1 time
Figure BDA00013154936000001317
Calculating the estimated value of the position of the target vehicle at the moment k +1
Figure BDA0001315493600000141
Comprises the following steps:
Figure BDA0001315493600000142
in an embodiment of the foregoing method for cooperatively locating multiple vehicles in an intelligent transportation system, the preset determination formula is further expressed as:
Figure BDA0001315493600000143
wherein, thetathreshIs a preset threshold value.
In S5, the judgment can be made by a predetermined judgment formula
Figure BDA0001315493600000144
Determination of target vehicle X0With adjacent vehicle XjRelative azimuth angle predicted value therebetween
Figure BDA0001315493600000145
Whether or not to approach
Figure BDA0001315493600000146
If approaching
Figure BDA0001315493600000147
The system state prediction value is retained
Figure BDA0001315493600000148
Namely the estimated value of the system state at the moment of k +1
Figure BDA0001315493600000149
If not close, k +1 time system state estimation
Figure BDA00013154936000001410
Comprises the following steps:
Figure BDA00013154936000001411
in the foregoing embodiment of the method for cooperative positioning of multiple vehicles in an intelligent transportation system, further, the method includes
Figure BDA00013154936000001412
Wherein,
Figure BDA00013154936000001413
is a target vehicle X0With adjacent vehicle XjThe variance of the relative azimuth angle therebetween.
As shown in fig. 2, a detailed description is given of the multi-vehicle cooperative positioning method in the intelligent transportation system according to this embodiment by using a specific example, and a matlab simulation platform is used to perform simulation analysis on the performance of the multi-vehicle cooperative positioning method in the intelligent transportation system according to this embodiment:
step 1, as shown in FIG. 2, consider a target vehicle X in an intelligent transportation system0And four adjacent vehicles X1,X2,X3,X4On the road, vehicle X0,X1,X2,X3,X4Respectively receiving respective position observation values at the k moments through own GPS equipment;
step 2, target vehicle X0Obtaining a motion observation value u of a vehicle-mounted sensor at the moment k0k＝[V0ka0kφ0k]TIf the target vehicle does linear motion, the target vehicle can be regarded as uniformly accelerated linear motion in the sampling period, namely a0kIs a constant value;
step 3, according to the target vehicle X0Obtaining the position observed value and the motion observed value of the target vehicle at the moment k +1, namely the target vehicle X0The motion model of (2); specifically, step 3 may include:
3.1) target vehicle X0The discrete equation of motion of the motion model of (1) is:
Figure BDA0001315493600000151
wherein, TsIs the sampling period.
3.2) according to the equation of discrete motion f (X)0k,u0k) Obtaining f (X)0k,u0k) With respect to X0kOf the jacobian matrix
Figure BDA0001315493600000152
Comprises the following steps:
Figure BDA0001315493600000153
3.3) according to the equation of discrete motion f (X)0k,u0k) Obtaining f (X)0k,u0k) About u0kOf the jacobian matrix
Figure BDA0001315493600000154
Comprises the following steps:
Figure BDA0001315493600000155
step 4, constructing the k time containing X through the step 30kWith adjacent vehicle X1k,X2k,X3k,X4kHas a system state of Xk＝[X0kX0kX0kX0k]TThe system input is uk＝[u0ku0ku0ku0k]TThen the system's equation of motion is Xk+1＝f(Xk,uk) (ii) a Specifically, step 4 may include:
4.1) Jacobian matrix according to step 3
Figure BDA0001315493600000156
Equation of motion f (X) of the resulting systemk,uk) About System State XkJacobian matrix akComprises the following steps:
Figure BDA0001315493600000161
4.2) Jacobian matrix according to step 3
Figure BDA0001315493600000162
Equation of motion f (X) of the resulting systemk,uk) About system input ukOf the Jacobian matrix BkComprises the following steps:
Figure BDA0001315493600000163
and 5: calculating to obtain the relative position information between the target vehicle and the adjacent vehicle, and constructing an observation equation Zk＝[Z1kZ2kZ3kZ4k]TSpecifically, step 5 may include:
5.1) k time target vehicle X0With adjacent vehicle XjRelative position information Z therebetweenjkComprises the following steps:
Figure BDA0001315493600000164
wherein d isjkIs a target vehicle X0With adjacent vehicle XjThe relative distance between the two or more of them,
Figure BDA0001315493600000165
is a target vehicle X0With adjacent vehicle XjRelative azimuth angle therebetween;
5.2) target vehicle X0Relative to Zjk＝hj(X0k,Xjk) Of the jacobian matrix HjkComprises the following steps:
Figure BDA0001315493600000166
5.3) according to the Jacobian matrix HjkAvailable observation equation ZkOf the jacobian matrix HkComprises the following steps:
Figure BDA0001315493600000171
step 6, the system state X obtained in the step 4 is processedkJacobian matrix akAnd system input ukOf the Jacobian matrix BkAnd the observation equation Z obtained in step 5kOf the jacobian matrix HkAnd substituting the motion equation and the observation equation of the system into the extended Kalman filtering to obtain the position estimation value of the target vehicle
Figure BDA0001315493600000172
Specifically, step 6 may include:
6.1) calculating the prediction value of the covariance of the system state
Figure BDA0001315493600000173
Figure BDA0001315493600000174
Wherein, PkIs the covariance of the system state at time k, Q is the covariance of the position observation error, TsIs a sampling period;
6.2) prediction of covariance from System State
Figure BDA0001315493600000175
Calculating K +1 moment Kalman filtering gain Kk+1：
Figure BDA0001315493600000176
Wherein R is an observation equation ZkError covariance of (H)kFor the observation of equation ZkA jacobian matrix.
6.3) Kalman filter gain K according to the K +1 momentk+1Obtaining the covariance P of the system state at the moment of k +1k+1：
Figure BDA0001315493600000177
Where I is the identity matrix, HkFor the observation of equation ZkA jacobian matrix.
6.4) according to the System State XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkComputing system state prediction values
Figure BDA0001315493600000178
Figure BDA0001315493600000179
Wherein,
Figure BDA00013154936000001710
and system observation equation prediction
Figure BDA00013154936000001711
Figure BDA0001315493600000181
Wherein,
Figure BDA0001315493600000182
6.5) calculating the estimated value of the system state at the moment k +1
Figure BDA0001315493600000183
The predicted value of the system observation equation obtained according to 6.4)
Figure BDA0001315493600000184
Determination of target vehicle X0With adjacent vehicle XjRelative azimuth angle predicted value therebetween
Figure BDA0001315493600000185
Whether or not to approach
Figure BDA0001315493600000186
If approaching
Figure BDA0001315493600000187
The system state prediction value is retained
Figure BDA0001315493600000188
Namely, it is
Figure BDA0001315493600000189
If not, then
Figure BDA00013154936000001810
Wherein,
Figure BDA00013154936000001811
Kk+1kalman filter gain at time k +1, Zk+1Is the observed value of the system observation equation at the moment k +1,
Figure BDA00013154936000001812
and the predicted value is the system state value at the moment k + 1.
6.6) calculated according to 6.5)
Figure BDA00013154936000001813
The estimated value of the position of the target vehicle at the moment of k +1 can be obtained
Figure BDA00013154936000001814
Comprises the following steps:
Figure BDA00013154936000001815
in this embodiment, as shown in fig. 3, fig. 3 is a schematic diagram illustrating a comparison between a position error measured by an embodiment of the present invention and a position error measured by a GPS, where an abscissa in fig. 3 is a sampling time and an ordinate is a position error size, and the result is obtained by performing 1000 experiments with the same conditions under a signal-to-noise ratio of 3 dB. As can be seen from fig. 3, the method proposed by the embodiment of the present invention reduces the position error and provides a more accurate position estimate. This shows that the cooperative positioning method proposed by the embodiment of the present invention is accurate and effective, and the comparison result also shows that the performance of the method adopted by the present invention is superior to the performance of the GPS measurement.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (5)

1. A multi-vehicle cooperative positioning method in an intelligent transportation system is characterized by comprising the following steps:
obtaining position observation values of a target vehicle and an adjacent vehicle in a system at the current moment, wherein the adjacent vehicle is a vehicle adjacent to the target vehicle;
obtaining a motion observation value of the target vehicle at the current moment;
calculating a position measurement value of the target vehicle at the next moment according to the obtained position observation value and the obtained motion observation value of the target vehicle at the current moment;
acquiring the number of the adjacent vehicles, and constructing a motion equation of the system by combining the acquired position observation value and motion observation value of the target vehicle at the current moment and the calculated position measurement value of the target vehicle at the next moment;
calculating relative position information between the target vehicle and the adjacent vehicle according to the acquired position observation values of the target vehicle and the adjacent vehicle at the current moment, and constructing an observation equation of the system according to the number of the adjacent vehicles;
substituting a motion equation and an observation equation of the system into the extended Kalman filtering to obtain a position estimation value of the target vehicle;
wherein the calculating a position measurement value of the target vehicle at the next moment according to the obtained position observation value and the obtained motion observation value of the target vehicle at the current moment comprises:
according to the target vehicle X0Position observation X at time k0kAnd the motion observation u0kAnd a target vehicle X0Calculating the target vehicle X0Position measurement X at time k +10(k+1)＝f(X0k,u0k) Wherein, f (X)0k,u0k) Indicating target vehicle X0Discrete equations of motion of the motion model of (a);
according to f (X)0k,u0k) Obtaining f (X)0k,u0k) Observed value X with respect to position0kOf the jacobian matrix
Figure FDA0002262908370000011
Comprises the following steps:
Figure FDA0002262908370000012
according to f (X)0k,u0k) Obtaining f (X)0k,u0k) Observed value u about movement0kOf the jacobian matrix
Figure FDA0002262908370000013
Comprises the following steps:
Figure FDA0002262908370000014
the acquiring the number of the adjacent vehicles, and combining the acquired position observed value and the acquired motion observed value of the target vehicle at the current moment and the calculated position measured value of the target vehicle at the next moment, the constructing a motion equation of the system comprises:
acquiring the number N of the adjacent vehicles;
combining the acquired position observed value X of the target vehicle at the time k according to the acquired number N of the adjacent vehicles0kAnd the motion observation u0kConstructing a system state of the entire system including the target vehicle and the neighboring vehicle at the time k
Figure FDA0002262908370000021
System input
Figure FDA0002262908370000022
The equation of motion of the system is Xk+1＝f(Xk,uk) (ii) a Wherein T represents a matrix transpose;
according to the Jacobian matrix AX0kTo obtain the motion equation f (X) of the systemk,uk) About System State XkJacobian matrix akComprises the following steps:
Figure FDA0002262908370000023
according to the Jacobian matrix
Figure FDA0002262908370000024
Obtaining the motion equation f (X) of the systemk,uk) About system input ukOf the Jacobian matrix BkComprises the following steps:
Figure FDA0002262908370000025
wherein, according to the obtained position observed values of the target vehicle and the adjacent vehicles at the current moment, calculating the relative position information between the target vehicle and the adjacent vehicles, and according to the number of the adjacent vehicles, constructing the observation equation of the system comprises:
according to the acquired k time, the target vehicle X0Is observed at a position of X0kAnd adjacent vehicle XjIs observed at a position of XjkCalculating the target vehicle X at the time k0And adjacent vehicle XjRelative position information Z therebetweenjk；
According to the calculated k time target vehicle X0And adjacent vehicle XjRelative position information Z therebetweenjkConstructing the observation equation Z of the systemk＝[Z1kZ2k…Zjk…Z(N-1)kZNk]TJ ═ 1,2, … N, where N represents the number of adjacent vehicles; wherein, Z isjkExpressed as:
Figure FDA0002262908370000031
wherein h isj(. h) a calculation formula representing relative positional information, djkIs a target vehicle X0With adjacent vehicle XjThe relative distance between the two or more of them,
Figure FDA0002262908370000032
is a target vehicle X0With adjacent vehicle XjRelative azimuth angle therebetween;
according to Zjk＝hj(X0k,Xjk) Target vehicle X0Relative to Zjk＝hj(X0k,Xjk) Of the jacobian matrix HjkComprises the following steps:
Figure FDA0002262908370000033
according to the Jacobian matrix HjkTo obtain an observation equation ZkOf the jacobian matrix HkComprises the following steps:
Figure FDA0002262908370000034
substituting the motion equation and the observation equation of the system into the extended Kalman filtering to obtain the position estimation value of the target vehicle comprises:
s1, according to the system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkAnd calculating the predicted value of the covariance of the system state at the moment of k +1
Figure FDA0002262908370000041
Wherein, PkThe covariance of the system state at time k, Q the covariance of the error of the position observation, T the matrix transposition, TsIs a sampling period;
s2, according to the observation equation ZkOf the jacobian matrix HkAnd the predicted value of the covariance of the system state at the moment of k +1 is obtained through calculation
Figure FDA0002262908370000042
Computing k +1 time Kalman filter gain
Figure FDA0002262908370000043
Wherein R is an observation equation ZkError covariance of (2);
s3, obtaining a Kalman filtering gain K according to the K +1 moment obtained by calculationk+1And calculating the covariance of the system state at the moment k +1
Figure FDA0002262908370000044
Wherein I is an identity matrix;
s4, according to the system state XkJacobian matrix akAnd system input ukOf the Jacobian matrix BkCalculating the predicted value of the system state at the moment k +1
Figure FDA0002262908370000045
And system observation equation prediction
Figure FDA0002262908370000046
Wherein,
the above-mentioned
Figure FDA0002262908370000047
And
Figure FDA0002262908370000048
respectively expressed as:
Figure FDA0002262908370000049
wherein,
Figure FDA00022629083700000410
n represents the number of adjacent vehicles;
Figure FDA00022629083700000411
wherein,
Figure FDA00022629083700000412
hj(. -) a calculation formula representing relative position information;
s5, according to the obtained system observation equation predicted value
Figure FDA00022629083700000413
Determination of target vehicle X0With adjacent vehicle XjRelative azimuth angle predicted value therebetween
Figure FDA00022629083700000414
Whether a preset judgment formula is met or not, and if so, determining the estimated value of the system state at the k +1 moment
Figure FDA00022629083700000415
If not, determining the estimated value of the system state at the k +1 moment
Figure FDA00022629083700000416
Comprises the following steps:
Figure FDA0002262908370000051
wherein,
Figure FDA0002262908370000052
Zk+1the observed value of the system observation equation at the moment k +1 is obtained;
s6, estimating the system state according to the k +1 time
Figure FDA0002262908370000053
Calculating the estimated value of the position of the target vehicle at the moment k +1
Figure FDA0002262908370000054
Comprises the following steps:
Figure FDA0002262908370000055
2. the method for cooperative positioning of multiple vehicles in an intelligent transportation system according to claim 1, wherein the obtaining of the position observation values of the target vehicle and the adjacent vehicle at the current time in the system comprises:
obtaining a target vehicle X at time k0Is observed at a position of X0k＝[x0ky0kθ0k]T；
Where k denotes the current time, T denotes the transpose, and x0kIndicating target vehicle X at time k0In the x-axis, y0kIndicating target vehicle X at time k0In the y-axis coordinate, θ0kIndicating target vehicle X at time k0The included angle formed by the motion direction and the x axis;
obtaining adjacent vehicles X at time kjIs observed at a position of Xjk＝[xjkyjkθjk]T，j＝1,2…N；
Wherein N represents the number of adjacent vehicles, xjkIndicating the adjacent vehicle X at time kjIn the x-axis, yjkIndicating the adjacent vehicle X at time kjIn the y-axis coordinate, θjkIndicating the adjacent vehicle X at time kjThe direction of motion forms an angle with the x-axis.
3. The method for cooperatively positioning multiple vehicles in the intelligent transportation system according to claim 1, wherein the obtaining of the observed value of the motion of the target vehicle at the current time comprises:
obtaining a target vehicle X at time k0Is observed in the movement u0k＝[V0ka0kφ0k]T；
Wherein, V0kIndicating target vehicle X at time k0A velocity of0kIndicating target vehicle X at time k0Acceleration of phi0kIndicating target vehicle X at time k0The steering angle of (c).
4. The method for the cooperative positioning of multiple vehicles in the intelligent transportation system according to claim 1, wherein the preset judgment formula is represented as:
Figure FDA0002262908370000061
wherein, thetathreshIs a preset threshold value.
5. The cooperative positioning method for multiple vehicles in intelligent transportation system as claimed in claim 4, wherein said method is characterized in that
Figure FDA0002262908370000062
Wherein,
Figure FDA0002262908370000063
is a target vehicle X0With adjacent vehicle XjThe variance of the relative azimuth angle therebetween.
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CN108398704B (en) * 2018-02-06 2020-11-06 北京科技大学 Bayesian filtering multi-vehicle cooperative positioning method
CN108519738A (en) * 2018-04-13 2018-09-11 中国科学院微电子研究所 Vehicle motion state information delay compensation method and device
FR3084151B1 (en) * 2018-07-23 2020-06-19 Safran METHOD AND DEVICE FOR AIDING THE NAVIGATION OF A FLEET OF VEHICLES USING AN INVARIANT KALMAN FILTER
CN111912413B (en) * 2020-07-23 2022-04-19 腾讯科技（深圳）有限公司 Positioning method and device
CN113706854B (en) * 2021-08-20 2023-03-07 北京科技大学 Vehicle cooperative positioning method in intelligent Internet of vehicles
CN113870489B (en) * 2021-09-10 2023-02-07 摩拜(北京)信息技术有限公司 Vehicle positioning method and device and vehicle
CN113963551B (en) * 2021-10-18 2022-09-27 中国电力科学研究院有限公司 Vehicle positioning method, system, device and medium based on cooperative positioning
CN114608590B (en) * 2022-03-09 2024-04-30 吉林大学 Multi-vehicle tracking method based on intelligent reflecting surface in severe environment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1934513A (en) * 2004-02-25 2007-03-21 学校法人立命馆 Control system of floating mobile body
CN102077259A (en) * 2009-02-27 2011-05-25 丰田自动车株式会社 Vehicle relative position estimation apparatus and vehicle relative position estimation method
CN102192745A (en) * 2010-02-24 2011-09-21 歌乐株式会社 Position estimation device and position estimation method
CN104282020A (en) * 2014-09-22 2015-01-14 中海网络科技股份有限公司 Vehicle speed detection method based on target motion track
CN105682222A (en) * 2016-03-01 2016-06-15 西安电子科技大学 Vehicle location positioning information fusion method based on vehicular ad hoc network

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016170635A1 (en) * 2015-04-23 2016-10-27 三菱電機株式会社 Leading vehicle selection assistance device, travel plan creation device, leading vehicle selection assistance method, and travel plan creation method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1934513A (en) * 2004-02-25 2007-03-21 学校法人立命馆 Control system of floating mobile body
CN102077259A (en) * 2009-02-27 2011-05-25 丰田自动车株式会社 Vehicle relative position estimation apparatus and vehicle relative position estimation method
CN102192745A (en) * 2010-02-24 2011-09-21 歌乐株式会社 Position estimation device and position estimation method
CN104282020A (en) * 2014-09-22 2015-01-14 中海网络科技股份有限公司 Vehicle speed detection method based on target motion track
CN105682222A (en) * 2016-03-01 2016-06-15 西安电子科技大学 Vehicle location positioning information fusion method based on vehicular ad hoc network

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