CN109147390B - Vehicle trajectory tracking method based on quantization adaptive Kalman filtering - Google Patents

Vehicle trajectory tracking method based on quantization adaptive Kalman filtering Download PDF

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CN109147390B
CN109147390B CN201810947274.XA CN201810947274A CN109147390B CN 109147390 B CN109147390 B CN 109147390B CN 201810947274 A CN201810947274 A CN 201810947274A CN 109147390 B CN109147390 B CN 109147390B
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vehicle
time
follows
acceleration
information
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CN109147390A (en
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钱丽萍
冯安琪
黄玉蘋
冯旭
黄亮
吴远
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Zhejiang University of Technology ZJUT
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Abstract

A vehicle speed prediction method based on quantization adaptive Kalman filtering comprises the following steps: firstly, in an intelligent network traffic system, automatically identifying running vehicles and acquiring related data through a DSRC technology, so as to realize information interaction between a vehicle-mounted system and a roadside unit; secondly, quantizing azimuth angles of the roadside unit and the vehicle-mounted system through a quantization formula, predicting acceleration through an autoregressive moving average method, and finally predicting horizontal position through adaptive Kalman filtering; and finally, sending the processed information to other 3 roadside units through an optical cable so as to facilitate information interaction with the vehicle-mounted system next time. The invention provides a vehicle trajectory tracking method based on quantitative adaptive Kalman filtering under an intelligent internet traffic system.

Description

Vehicle trajectory tracking method based on quantization adaptive Kalman filtering
Technical Field
The invention belongs to the field of traffic, and particularly relates to a vehicle trajectory tracking method based on quantization adaptive Kalman filtering in an intelligent network connection traffic system.
Background
China is the country with the most population in the world, the reform is open, along with the rapid development of the economy of China, the living standard of people is increasingly improved, private cars begin to enter each family, and the traveling of the family is facilitated well. However, the popularization and popularization of vehicles also make the urban traffic environment worsen day by day, and traffic phenomena such as unbalanced traffic flow, traffic congestion, rear collision, side collision and the like occur. Along with the weak infrastructure and the traffic network congestion, the number of road traffic accidents is increasing, and the high incidence of the traffic accidents is sounding an alarm to the whole society, so that the road traffic safety is greatly concerned. In recent years, although the road infrastructure and the traffic network are greatly improved in China, the number of traffic accidents and casualties are reduced, but the total number of accidents and the incidence rate are still high.
Compared with the traditional road traffic system, the intelligent internet traffic system is more prone to a dynamic system in which information interaction is carried out by people, roads, vehicles, road traffic facilities and the like. According to a large amount of statistical research in various countries, it is found that driver error is a main factor causing traffic accidents. Therefore, under the condition that the current road infrastructure can not be improved, the work of acquiring the state information of the vehicles in other lanes of the road, processing and broadcasting the state information to the current vehicle is not slow, so that a driver can better take corresponding remedial measures, and traffic accidents caused by errors of the driver are reduced.
Disclosure of Invention
In order to overcome the defects of low safety and high traffic accident occurrence probability of the conventional road traffic system, the invention provides a vehicle trajectory tracking method based on quantitative adaptive Kalman filtering in an intelligent internet traffic system.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a vehicle trajectory tracking method based on quantization adaptive Kalman filtering comprises the following steps:
1) in an intelligent network traffic system, a vehicle is automatically identified through a DSRC technology, relevant data are obtained, and information interaction between a vehicle-mounted system and a roadside unit is realized, wherein the information interaction comprises the following steps:
step 1.1: when a running vehicle enters the range covered by the directional antenna, the vehicle-mounted system and the roadside unit realize two-way communication through the DSRC technology, so that the two sides can simultaneously transmit information on the storage units of the vehicle-mounted system, wherein the information transmitted by the vehicle-mounted system comprises the current speed, the current position and the time stamp of the vehicle, and the information transmitted by the roadside unit comprises the predicted position of the vehicle on other lanes, the direction in which the vehicle is located, several lanes and the acceleration;
step 1.2: the roadside unit sends the acquired vehicle information to an edge cloud server to perform a series of operation operations;
2) the edge cloud server calculates an azimuth angle and performs quantization processing according to the azimuth difference between the roadside unit and the vehicle-mounted system, and quantizes the vehicle driving direction according to the position information, wherein the quantization process comprises the following steps:
step 2.1: the position information is converted into digital information existing in a viewing angle. Wherein the actual carrying angle of the vehicle
Figure BDA0001770564290000021
The definition with respect to a roadside unit is:
Figure BDA0001770564290000022
Figure BDA0001770564290000031
here, the parameters are defined as follows:
Figure BDA0001770564290000032
azimuth angle between roadside unit and vehicle system at time t;
θt: converting the vehicle position at the time t into an inverse trigonometric function of the azimuth angle;
σt: bearing error noise caused by signal reflection at time t;
(xt,yt): the current position of the vehicle at time t;
step 2.2: using the center of the crossroad as the origin of coordinates and the angle of opposite direction
Figure BDA0001770564290000033
And performing quantization processing to determine the direction of the vehicle, wherein the quantization formula is as follows:
Figure BDA0001770564290000034
here, the parameters are defined as follows:
bt: the quantitative direction of the vehicle at the crossroad at the moment t;
q (·): a quantization function;
i: direction identification of the crossroad;
step 2.3: to specify the direction information, the lane of the direction is quantized, and the actual bearing angle is set
Figure BDA0001770564290000035
Rename it to
Figure BDA0001770564290000036
It is recorded as:
Figure BDA0001770564290000037
Figure BDA0001770564290000038
here, the parameters are defined as follows:
Figure BDA0001770564290000041
azimuth angle between roadside unit and vehicle system at time t;
θt': at the moment t, converting the relative position of the vehicle and the roadside unit into an inverse trigonometric function of the azimuth angle;
(xi,yi): the fixed position of the roadside unit in the direction i;
step 2.4: according to the quantization formula, performing second quantization on the direction of the vehicle to determine the lane of the vehicle;
Figure BDA0001770564290000042
here, the parameters are defined as follows:
qt: the quantized direction of the lane at time t;
ji: the jth lane in direction i;
n: total number of lanes;
3) assuming that only the latest p +1 vehicle speeds are used for acceleration estimation, the pth acceleration calculation method is:
Figure BDA0001770564290000043
here, the parameters are defined as follows:
Δ τ: a sampling time interval;
Δ v: the difference in velocity between the next time and the previous time;
vt-p: the speed of the trolley at the time t-p;
τt-p: a timestamp of the trolley at the time t-p;
at-p: the p-th acceleration value;
thereafter, vehicle acceleration prediction is performed by using an autoregressive moving average method according to the p acceleration values, wherein the prediction formula is as follows:
Figure BDA0001770564290000044
here, the parameters are defined as follows:
at: acceleration of the trolley at time t;
p: the autoregressive order, namely the total acceleration;
q: moving average order, i.e., total number of slips;
β, undetermined coefficient not equal to zero;
Figure BDA0001770564290000051
undetermined coefficients other than zero;
ξt: an error term independent at time t;
4) assuming that a driver does not change a lane, aiming at collected relevant information, a horizontal position of a running vehicle is predicted by using an adaptive Kalman filtering algorithm, wherein a calculation formula of the horizontal position is as follows:
Figure BDA0001770564290000052
converting the above formula into a state equation and an observation equation by using a state space model, wherein the equations are as follows:
xt+1＝Atxt+Btut+ωt；
zt＝Ctxt+εt；
here, the parameters are defined as follows:
xt: the state vector of the trolley at the moment t;
At,Bt,Ct: a state transition matrix at time t;
ut: acceleration of the trolley at time t;
ωt: the system error at the time t follows Gaussian distributionN(0,Qt) Wherein Q istIs the process noise variance at time t;
εt: measurement error at time t obeys Gaussian distribution N (0, R)t) Wherein R istIs the measured noise variance at time t;
zt: a state observation of the system at time t;
and then, predicting the horizontal position of the vehicle by using an adaptive Kalman filtering algorithm according to the state space model, wherein the vehicle position is updated by the following steps:
step 4.1: giving an initial value
Figure BDA0001770564290000061
And
Figure BDA0001770564290000062
wherein,
Figure BDA0001770564290000063
showing the predicted value of the horizontal position of the trolley at the moment t,
Figure BDA0001770564290000064
representing the error covariance of the trolley at the time t;
step 4.2: according to a given initial value
Figure BDA0001770564290000065
Calculating Kalman gain value K at t momenttWherein
Figure BDA0001770564290000066
step 4.3: predicted value according to time t
Figure BDA0001770564290000067
And the observed value ztThe corrected value of the current state can be obtained
Figure BDA0001770564290000068
Wherein, the formula is as follows:
zt＝Ctxt+Rt
Figure BDA0001770564290000069
step 4.4: updating error covariance
Figure BDA00017705642900000610
In preparation for predicting the error covariance at time t +1, wherein,
Figure BDA00017705642900000611
i is a unit array;
step 4.5: correction value according to time t
Figure BDA00017705642900000612
And acceleration utTo predict the horizontal position of the trolley at the moment t +1
Figure BDA00017705642900000613
Wherein,
Figure BDA00017705642900000614
step 4.6: prediction of error covariance from the error covariance of the previous time instant
Figure BDA00017705642900000615
To predict the error covariance of the next time instant
Figure BDA00017705642900000616
Wherein,
Figure BDA00017705642900000617
μt+1is an adaptive forgetting factor;
step 4.7: calculating the acceleration u at time t +1t+1Wherein, the calculation method is the autoregressive moving average method in the 3);
step 4.8: updating the iteration number k to k +1, and returning to the step 4.2 to start a new round of calculation;
5) and finally, the edge cloud server transmits the processed information to the roadside unit through an optical cable so as to facilitate the information interaction with the vehicle-mounted system next time.
Further, in step 4.6, the calculation formula of the adaptive forgetting factor is as follows:
μt+1＝max{1,tr(Nt+1)/tr(Mt+1)}；
Figure BDA0001770564290000071
Figure BDA0001770564290000072
Figure BDA0001770564290000073
Figure BDA0001770564290000074
here, the parameters are defined as follows:
Nt+1: the error variance at the moment of t +1 ensures that the values are symmetrical and positive;
Lt+1: covariance matrix of innovation at time t + 1;
et+1: the difference between the measured value and the predicted value at the moment t +1 is the innovation;
Figure BDA0001770564290000075
transposing the state transition matrix C at time t + 1;
Mt+1: error variance at time t +1, ensuring error covariance
Figure BDA0001770564290000076
The values of (a) are symmetrical and positive;
max {. cndot ]: and taking the maximum value after comparison.
Furthermore, in the step 1), in the intelligent internet traffic system, the roadside units are installed on traffic lights at the crossroad and are attached with the cloud server and the directional antenna, wherein the emission angle of the routing antenna is 60 degrees, so that the roadside units can better perform information interaction with a vehicle-mounted system in a vehicle.
Still further, in step 1.2, the data in the server is cleared every other week in consideration of the limited storage capacity of the edge cloud server.
The technical conception of the invention is as follows: firstly, in an intelligent network traffic system, a vehicle is automatically identified through a DSRC technology, and relevant data are acquired, so that information interaction between a vehicle-mounted system and a roadside unit is realized. Secondly, quantizing the azimuth angles of the roadside unit and the vehicle-mounted system by using a quantization formula according to the collected related information; predicting the acceleration by using an autoregressive moving average method; and performing horizontal position prediction by using the adaptive Kalman filtering. And finally, transmitting the processed information to other 3 roadside units through an optical cable so as to facilitate information interaction with the vehicle-mounted system next time.
The invention has the advantages that 1, the lanes in which the current vehicle is positioned can be clearly known through quantifying the azimuth difference between the roadside unit and the vehicle-mounted system. 2. The prediction of the acceleration and the position is realized by combining an autoregressive moving average method and an adaptive Kalman filtering method, and the result is transmitted to the driver, so that the driver can make proper judgment and behavior according to the relevant information of the vehicle and own experience, and the occurrence rate of traffic accidents is effectively reduced.
Drawings
Fig. 1 is a schematic diagram of mobile internet traffic system information interaction.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
Referring to fig. 1, a vehicle trajectory tracking method based on quantization adaptive kalman filtering is disclosed, wherein the invention is based on an information interaction model (as shown in fig. 1) under DSRC technical communication. In the intelligent network traffic system, firstly, the azimuth angles of a roadside unit and a vehicle-mounted system are quantized through a quantization formula, secondly, the acceleration is predicted through an autoregressive moving average method, and finally, the position is predicted through adaptive Kalman filtering; the method comprises the following steps:
1) in an intelligent network traffic system, a vehicle is automatically identified through a DSRC technology, relevant data are obtained, and information interaction between a vehicle-mounted system and a roadside unit is realized, wherein the information interaction comprises the following steps:
step 1.1: when a running vehicle enters the range covered by the directional antenna, the vehicle-mounted system and the roadside unit realize two-way communication through the DSRC technology, so that the two sides can simultaneously transmit information on the storage units of the vehicle-mounted system, wherein the information transmitted by the vehicle-mounted system comprises the current speed, the current position and the time stamp of the vehicle, and the information transmitted by the roadside unit comprises the predicted position of the vehicle on other lanes, the direction in which the vehicle is located, several lanes and the acceleration;
step 1.2: the roadside unit sends the acquired vehicle information to an edge cloud server to perform a series of operation operations;
2) the edge cloud server calculates an azimuth angle and performs corresponding quantization processing according to the azimuth difference between the roadside unit and the vehicle-mounted system, and quantizes the vehicle driving direction according to the position information, wherein the quantization process comprises the following steps:
step 2.1: converting the position information into digital information which is present at a viewing angle, wherein the actual bearing angle of the vehicle
Figure BDA0001770564290000091
The definition with respect to a roadside unit is:
Figure BDA0001770564290000092
Figure BDA0001770564290000093
here, the parameters are defined as follows:
Figure BDA0001770564290000094
azimuth angle between roadside unit and vehicle system at time t;
θt: converting the vehicle position at the time t into an inverse trigonometric function of the azimuth angle;
σt: bearing error noise caused by signal reflection at time t;
(xt,yt): the current position of the vehicle at time t;
step 2.2: using the center of the crossroad as the origin of coordinates and the angle of opposite direction
Figure BDA0001770564290000101
And performing quantization processing to determine the direction of the vehicle, wherein the quantization formula is as follows:
Figure BDA0001770564290000102
here, the parameters are defined as follows:
bt: the quantitative direction of the vehicle at the crossroad at the moment t;
q (·): a quantization function;
i: direction identification of the crossroad;
step 2.3: to materialize the direction information, the lanes of the direction are quantized. Will actually angle the bearing
Figure BDA0001770564290000103
Rename it to
Figure BDA0001770564290000104
It is recorded as:
Figure BDA0001770564290000105
Figure BDA0001770564290000106
here, the parameters are defined as follows:
Figure BDA0001770564290000107
azimuth angle between roadside unit and vehicle system at time t;
θt': at the moment t, converting the relative position of the vehicle and the roadside unit into an inverse trigonometric function of the azimuth angle;
(xi,yi): the fixed position of the roadside unit in the direction i;
step 2.4: according to the quantization formula, performing second quantization on the direction of the vehicle to determine the lane of the vehicle;
Figure BDA0001770564290000108
here, the parameters are defined as follows:
qt: the quantized direction of the lane at time t;
ji: the jth lane in direction i;
n: total number of lanes;
3) assuming that only the latest p +1 vehicle speeds are used for acceleration estimation, the pth acceleration calculation method is:
Figure BDA0001770564290000111
here, the parameters are defined as follows:
Δ τ: a sampling time interval;
Δ v: the difference in velocity between the next time and the previous time;
vt-p: the speed of the trolley at the time t-p;
τt-p: a timestamp of the trolley at the time t-p;
at-p: the p-th acceleration value;
thereafter, vehicle acceleration prediction is performed by using an autoregressive moving average method according to the p acceleration values, wherein the prediction formula is as follows:
Figure BDA0001770564290000112
here, the parameters are defined as follows:
at: acceleration of the trolley at time t;
p: the autoregressive order, namely the total acceleration;
q: moving average order, i.e., total number of slips;
β, undetermined coefficient not equal to zero;
Figure BDA0001770564290000113
undetermined coefficients other than zero;
ξt: an error term independent at time t;
4) assuming that a driver does not change a lane, aiming at collected relevant information, a horizontal position of a running vehicle is predicted by using an adaptive Kalman filtering algorithm, wherein a calculation formula of the horizontal position is as follows:
Figure BDA0001770564290000121
converting the above formula into a state equation and an observation equation by using a state space model, wherein the equations are as follows:
xt+1＝Atxt+Btut+ωt；
zt＝Ctxt+εt；
here, the parameters are defined as follows:
xt: the state vector of the trolley at the moment t;
At,Bt,Ct: a state transition matrix at time t;
ut: acceleration of the trolley at time t;
ωt: the systematic error at time t obeys the Gaussian distribution N (0, Q)t) Wherein Q istIs the process noise variance at time t;
εt: measurement error at time t obeys Gaussian distribution N (0, R)t) Wherein R istIs the measured noise variance at time t;
zt: a state observation of the system at time t;
and then, predicting the horizontal position of the vehicle by using an adaptive Kalman filtering algorithm according to the state space model, wherein the vehicle position is updated by the following steps:
step 4.1: giving an initial value
Figure BDA0001770564290000122
And
Figure BDA0001770564290000123
wherein,
Figure BDA0001770564290000124
showing the predicted value of the horizontal position of the trolley at the moment t,
Figure BDA0001770564290000125
representing the error covariance of the trolley at the time t;
step 4.2: according to a given initial value
Figure BDA0001770564290000126
Calculating Kalman gain value K at t momenttWherein
Figure BDA0001770564290000127
step 4.3: predicted value according to time t
Figure BDA0001770564290000128
And the observed value ztThe corrected value of the current state can be obtained
Figure BDA0001770564290000129
Wherein, the formula is as follows:
zt＝Ctxt+Rt
Figure BDA0001770564290000131
step 4.4: updating error covariance
Figure BDA0001770564290000132
In preparation for predicting the error covariance at time t +1, wherein,
Figure BDA0001770564290000133
i is a unit array;
step 4.5: correction value according to time t
Figure BDA0001770564290000134
And acceleration utTo predict the horizontal position of the trolley at the moment t +1
Figure BDA0001770564290000135
Wherein,
Figure BDA0001770564290000136
step 4.6: prediction of error covariance from the error covariance of the previous time instant
Figure BDA0001770564290000137
To predict the error covariance of the next time instant
Figure BDA0001770564290000138
Wherein,
Figure BDA0001770564290000139
μt+1is an adaptive forgetting factor;
step 4.7: calculating the acceleration u at time t +1t+1Wherein, the calculation method is the autoregressive moving average method in the 3);
step 4.8: updating the iteration number k to k +1, and returning to the step 4.2 to start a new round of calculation;
5) and finally, the edge cloud server transmits the processed information to the roadside unit through an optical cable so as to facilitate the information interaction with the vehicle-mounted system next time.
Further, in step 4.6, the calculation formula of the adaptive forgetting factor is as follows:
μt+1＝max{1,tr(Nt+1)/tr(Mt+1)}；
Figure BDA00017705642900001310
Figure BDA00017705642900001311
Figure BDA00017705642900001312
Figure BDA00017705642900001313
here, the parameters are defined as follows:
Nt+1: the error variance at the moment of t +1 ensures that the values are symmetrical and positive;
Lt+1: covariance matrix of innovation at time t + 1;
et+1: the difference between the measured value and the predicted value at the moment t +1 is the innovation;
Figure BDA0001770564290000141
transposing the state transition matrix C at time t + 1;
Mt+1: error variance at time t +1, ensuring error covariance
Figure BDA0001770564290000142
The values of (a) are symmetrical and positive;
max {. cndot ]: and taking the maximum value after comparison.
Furthermore, in the step 1), in the intelligent internet traffic system, the roadside units are installed on traffic lights at the crossroad and are attached with the cloud server and the directional antenna, wherein the emission angle of the routing antenna is 60 degrees, so that the roadside units can better perform information interaction with a vehicle-mounted system in a vehicle.
Still further, in step 1.2, the data in the server is cleared every other week in consideration of the limited storage capacity of the edge cloud server.

Claims (4)

1. A vehicle trajectory tracking method based on quantization adaptive Kalman filtering is characterized by comprising the following steps:
1) in an intelligent network traffic system, a vehicle is automatically identified through a DSRC technology, relevant data are obtained, and information interaction between a vehicle-mounted system and a roadside unit is realized, wherein the information interaction comprises the following steps:
step 1.1: when a running vehicle enters the range covered by the directional antenna, the vehicle-mounted system and the roadside unit realize two-way communication through the DSRC technology, so that the two sides can simultaneously transmit information on the storage units of the vehicle-mounted system, wherein the information transmitted by the vehicle-mounted system comprises the current speed, the current position and the time stamp of the vehicle, and the information transmitted by the roadside unit comprises the predicted position of the vehicle on other lanes, the direction in which the vehicle is located, several lanes and the acceleration;
step 1.2: the roadside unit sends the acquired vehicle information to an edge cloud server to perform a series of operation operations;
2) the edge cloud server calculates an azimuth angle and performs corresponding quantization processing according to the azimuth difference between the roadside unit and the vehicle-mounted system, and quantizes the vehicle driving direction according to the position information, wherein the quantization process comprises the following steps:
step 2.1: converting the position information into digital information that exists at a viewing angle, wherein the direction angle of the vehicle
Figure FDA0002390146990000011
The definition with respect to a roadside unit is:
Figure FDA0002390146990000012
；
Figure FDA0002390146990000021
here, the parameters are defined as follows:
Figure FDA0002390146990000022
a direction angle between the roadside unit and the vehicle-mounted system at time t;
θt: converting the vehicle position at the time t into an inverse trigonometric function of the azimuth angle;
σt: bearing error noise caused by signal reflection at time t;
(xt,yt): the current position of the vehicle at time t;
step 2.2: using the center of the crossroad as the origin of coordinates and the angle of opposite direction
Figure FDA0002390146990000023
And performing quantization processing to determine the direction of the vehicle, wherein the quantization formula is as follows:
Figure FDA0002390146990000024
here, the parameters are defined as follows:
bt: the quantitative direction of the vehicle at the crossroad at the moment t;
q (·): a quantization function;
i: direction identification of the crossroad;
step 2.3: to specify the direction information, the lane of the direction is quantized and the direction angle is set
Figure FDA0002390146990000025
Rename it to
Figure FDA0002390146990000026
It is recorded as:
Figure FDA0002390146990000027
Figure FDA0002390146990000028
here, the parameters are defined as follows:
θt': at the moment t, converting the relative position of the vehicle and the roadside unit into an inverse trigonometric function of the azimuth angle;
(xi,yi): the fixed position of the roadside unit in the direction i;
step 2.4: according to the quantization formula, performing second quantization on the direction of the vehicle to determine the lane of the vehicle;
Figure FDA0002390146990000031
here, the parameters are defined as follows:
qt: the quantized direction of the lane at time t;
ji: the jth lane in direction i;
n: total number of lanes;
3) assuming that only the latest p +1 vehicle speeds are used for acceleration estimation, the pth acceleration calculation method is:
Figure FDA0002390146990000032
here, the parameters are defined as follows:
Δ τ: a sampling time interval;
Δ v: the difference in velocity between the next time and the previous time;
vt-p: the speed of the trolley at the time t-p;
τt-p: a timestamp of the trolley at the time t-p;
at-p: the p-th acceleration value;
thereafter, vehicle acceleration prediction is performed by using an autoregressive moving average method according to the p acceleration values, wherein the prediction formula is as follows:
Figure FDA0002390146990000033
here, the parameters are defined as follows:
at: acceleration of the trolley at time t;
p: the autoregressive order, namely the total acceleration;
q: moving average order, i.e., total number of slips;
β, undetermined coefficient not equal to zero;
Figure FDA0002390146990000042
undetermined coefficients other than zero;
ξt: an error term independent at time t;
4) assuming that a driver does not change a lane, aiming at collected relevant information, a horizontal position of a running vehicle is predicted by using an adaptive Kalman filtering algorithm, wherein a calculation formula of the horizontal position is as follows:
Figure FDA0002390146990000041
converting the above formula into a state equation and an observation equation by using a state space model, wherein the equations are as follows:
xt+1＝Atxt+Btut+ωt；
zt＝Ctxt+εt；
here, the parameters are defined as follows:
xt: the state vector of the trolley at the moment t;
At,Bt,Ct: a state transition matrix at time t;
ut: acceleration of the trolley at time t;
ωt: the systematic error at time t obeys the Gaussian distribution N (0, Q)t) Wherein Q istIs the process noise variance at time t;
εt: measurement error at time t obeys Gaussian distribution N (0, R)t) Wherein R istIs the measured noise variance at time t;
zt: a state observation of the system at time t;
and then, predicting the horizontal position of the vehicle by using an adaptive Kalman filtering algorithm according to the state space model, wherein the vehicle position is updated by the following steps:
step 4.1: giving an initial value
Figure FDA0002390146990000051
And
Figure FDA0002390146990000052
wherein,
Figure FDA0002390146990000053
showing the predicted value of the horizontal position of the trolley at the moment t,
Figure FDA0002390146990000054
representing the error covariance of the trolley at the time t;
step 4.2: according to a given initial value
Figure FDA0002390146990000055
Calculating Kalman gain value K at t momenttWherein
Figure FDA0002390146990000056
step 4.3: predicted value according to time t
Figure FDA0002390146990000057
And the observed value ztThe corrected value of the current state can be obtained
Figure FDA0002390146990000058
Wherein, the formula is as follows:
zt＝Ctxt+Rt
Figure FDA0002390146990000059
step 4.4: updating error covariance
Figure FDA00023901469900000510
In preparation for predicting the error covariance at time t +1, wherein,
Figure FDA00023901469900000511
i is a unit array;
step 4.5: correction value according to time t
Figure FDA00023901469900000512
And acceleration utTo predict the horizontal position of the trolley at the moment t +1
Figure FDA00023901469900000513
Wherein,
Figure FDA00023901469900000514
step 4.6: prediction of error covariance from the error covariance of the previous time instant
Figure FDA00023901469900000515
To predict the error covariance of the next time instant
Figure FDA00023901469900000516
Wherein,
Figure FDA00023901469900000517
μt+1is an adaptive forgetting factor;
step 4.7: calculating the acceleration u at time t +1t+1Wherein, the calculation method is the autoregressive moving average method in the step 3);
step 4.8: updating the iteration number k to k +1, and returning to the step 4.2 to start a new round of calculation;
5) and finally, the edge cloud server transmits the processed information to the roadside unit through an optical cable so as to facilitate the information interaction with the vehicle-mounted system next time.
2. The vehicle trajectory tracking method based on the quantized adaptive kalman filter according to claim 1, wherein: in the step 1), in the intelligent network traffic system, the roadside units are installed on traffic lights of the crossroads and are attached with edge cloud servers and directional antennas, wherein the emission angle of the directional antennas is 60 degrees, so that the roadside units can better perform information interaction with vehicle-mounted systems in the vehicles.
3. The vehicle trajectory tracking method based on the quantized adaptive kalman filter according to claim 1 or 2, wherein: in step 4.6, the calculation formula of the adaptive forgetting factor is as follows:
μt+1＝max{1,tr(Nt+1)/tr(Mt+1)}；
Figure FDA0002390146990000061
Figure FDA0002390146990000062
Figure FDA0002390146990000063
Figure FDA0002390146990000064
here, the parameters are defined as follows:
Nt+1: the error variance at the moment of t +1 ensures that the values are symmetrical and positive;
Lt+1: covariance matrix of innovation at time t + 1;
et+1: the difference between the measured value and the predicted value at the moment t +1 is the innovation;
Figure FDA0002390146990000065
transposing the state transition matrix C at time t + 1;
Mt+1: error variance at time t +1, ensuring error covariance
Figure FDA0002390146990000066
The values of (a) are symmetrical and positive;
max {. cndot ]: and taking the maximum value after comparison.
4. The vehicle trajectory tracking method based on the quantized adaptive kalman filter according to claim 1 or 2, wherein: in step 1.2, the data in the server is cleared every other week in consideration of the limited storage capacity of the edge cloud server.
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