CN111308458A - Vehicle speed estimation method based on vehicle-mounted millimeter wave radar - Google Patents
Vehicle speed estimation method based on vehicle-mounted millimeter wave radar Download PDFInfo
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- CN111308458A CN111308458A CN202010108361.3A CN202010108361A CN111308458A CN 111308458 A CN111308458 A CN 111308458A CN 202010108361 A CN202010108361 A CN 202010108361A CN 111308458 A CN111308458 A CN 111308458A
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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Abstract
The invention discloses a vehicle speed estimation method based on a vehicle-mounted millimeter wave radar. The method comprises the following steps: receiving a reflected wave signal from a forward target by a vehicle-mounted high-precision millimeter wave radar, obtaining relative speed information of the target in a view field by a radar signal processing technology, and updating a vehicle speed estimation result once at intervals; performing relevant filtering processing on all speed detection results in a single updating period, and matching confidence coefficients on each speed estimation result; and performing smooth filtering processing on the vehicle speed estimation result in a plurality of updating periods to obtain the vehicle speed estimation result with corresponding confidence coefficient. The invention has the advantages of high real-time performance, high precision and strong universality.
Description
Technical Field
The invention belongs to the technical field of vehicle speed estimation, and particularly relates to a vehicle speed estimation method based on a vehicle-mounted millimeter wave radar.
Background
With the development of intelligent transportation systems, people are increasingly concerned about safe driving of automobiles. In the process of vehicle running, accurate sensing of the speed of the vehicle can help a driver to control and adjust the running state of the vehicle, and is an important step for realizing safe driving. Therefore, as important information in an active safety system of a vehicle, real-time and accurate estimation of the longitudinal speed of the vehicle becomes an urgent problem to be solved.
In the prior art, an OBD interface is used to access a bus system inside a vehicle through a vehicle-mounted terminal with satellite positioning and mobile communication functions installed on the vehicle, so as to obtain status data of the vehicle. The OBD speed information of the vehicle is obtained through the OBD bus at a fixed time interval delta T, and good measurement accuracy is obtained. However, this design has the disadvantage that the time interval Δ T is limited to be large during the vehicle speed acquisition process, and the vehicle speed estimation result cannot be output in real time.
Disclosure of Invention
In view of this, the present invention provides a method for estimating the speed of a vehicle based on a vehicle-mounted millimeter wave radar, which can realize real-time accurate estimation of the speed of the vehicle.
A vehicle speed estimation method based on a vehicle-mounted millimeter wave radar is characterized by comprising the following steps:
s1: the vehicle to be tested runs stably along the highway at any speed, and a periodic FMCW waveform is transmitted by a 77GHz vehicle-mounted high-precision millimeter wave radar;
s2: the radar receives an echo signal and performs two-dimensional FFT on single-frame echo data to obtain an R-D spectrum;
s3: solving the distance, the radial speed and the azimuth angle of each target;
s4: calculating the relative speed of the target and the normal direction of the vehicle to be measured:
wherein, VrAnd theta are obtained in step S3β is radar installation angle;
s5: counting the speed information of the target group by using a speed discrimination algorithm, setting a speed error threshold Verror to classify the targets, screening moving targets, and performing confidence matching to obtain target group results with the number of the targets ranked first three;
firstly, sorting the targets according to the target speed;
secondly, dividing the target speed with the speed error within the Verror into a cluster based on a set speed error threshold Verror;
thirdly, estimating the average value of the speed in each cluster of targets;
finally, setting a speed estimation confidence coefficient according to the number of targets in each cluster, wherein the more the number of the targets is, the higher the speed estimation confidence coefficient of the cluster is; reserving clusters with the number of targets ranked first three in the clusters;
s6: according to a set radar measuring target speed range, removing target clusters exceeding a speed boundary in clusters with the number of targets ranked in the first three;
s7: estimating the speed of the bicycle:
firstly, judging: whether a target cluster with the speed estimation confidence coefficient higher than a set threshold exists in the target clusters, if so, directly outputting the speed mean value estimation result of the target cluster meeting the conditions as the speed estimation result of the vehicle;
if the target clusters which meet the conditions do not exist, calculating the error between the current speed estimation value of each target cluster and the estimated speed of the vehicle in the previous two frames, and outputting the speed average value of the target cluster with the minimum error as the estimation result of the speed of the vehicle;
and when the situations are not met, selecting the average speed of the target cluster with the maximum number of targets as a vehicle speed estimation result for output.
In the step, the smooth filtering process sets a sliding window with a fixed length, and the sliding window slides backwards along with the delay of the number of frames, so that data in the window is filtered smoothly, and the estimation error of the speed of the vehicle can be reduced.
Further, after the multi-frame own vehicle speed estimation is completed, the adjacent multi-frame own vehicle speed estimation result is subjected to smooth filtering processing, and finally the own vehicle speed estimation result is determined.
Preferably, in step S3, the velocity of the target can be calculated according to the result of the R-D spectral doppler dimension, and the formula is as follows:
wherein, TcThe time interval of adjacent transmitted wave signals is defined, lambda is the wavelength corresponding to the initial frequency of the transmitted wave, and omega is the phase interval of two continuous transmitted wave signals;
the angle of the target is calculated according to the following formula:
where Δ d is the distance between adjacent receiving antennas, and λ is the wavelength corresponding to the initial frequency of the transmitted wave.
The invention has the following beneficial effects:
the invention discloses a vehicle speed estimation method based on a vehicle-mounted millimeter wave radar. The method comprises the following steps: receiving a reflected wave signal from a forward target by a vehicle-mounted high-precision millimeter wave radar, obtaining relative speed information of the target in a view field by a radar signal processing technology, and updating a vehicle speed estimation result once at intervals; performing relevant filtering processing on all speed detection results in a single updating period, and matching confidence coefficients on each speed estimation result; and performing smooth filtering processing on the vehicle speed estimation result in a plurality of updating periods to obtain the vehicle speed estimation result with corresponding confidence coefficient. The invention has the advantages of high real-time performance, high precision and strong universality.
Drawings
FIG. 1 is a schematic diagram of an apparatus for a vehicle-mounted radar speed estimation method according to the present invention;
FIG. 2 is a schematic diagram of processing results of the vehicle-mounted radar self-speed estimation algorithm.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
In fig. 1, the radar is arranged in front of a vehicle, the vehicle starts to run after a radar coordinate system is aligned with a vehicle coordinate system, M2 and M8 are results of radar detection of moving targets, and M1, M3, M4, M5, M6 and M7 are results of radar detection of static targets. Obtaining the speed of a static target according to a target speed screening algorithm, and further obtaining the estimation of the speed of the vehicle according to a geometric relation, wherein the method comprises the following specific steps:
s1: the vehicle to be tested runs stably along the highway at any speed, and a periodic FMCW waveform is transmitted by a 77GHz vehicle-mounted high-precision millimeter wave radar;
s2: the radar receives an echo signal and performs two-dimensional FFT on single-frame echo data to obtain an R-D spectrum; in this step, a single frame includes pulses of N antenna receiving channels, each receiving channel includes M pulse repetition periods, and each pulse repetition period includes P sampling points.
S3: obtaining an R-D spectrum by using each frame of echo, calculating the distance and radial speed of each target, performing fft based on multi-channel data of the target point to obtain an angle dimension one-dimensional image, and further obtaining the azimuth angle of each target;
in this step, the distance of the target in the field of view is calculated according to the following formula:
wherein: d is the target distance, fIFThe target intermediate frequency signal frequency is shown, c is the speed of light, and S is the frequency modulation slope of the radar transmitted linear frequency modulation signal.
The velocity of the target can be calculated according to the Doppler dimension result of the R-D spectrum, and the basic formula is as follows:
wherein, TcThe time interval of adjacent emission wave signals is represented by λ, which is the wavelength corresponding to the initial frequency of the emission wave, and ω, which is the phase interval of two consecutive emission wave signals.
The angle of the target is calculated according to the following formula:
where Δ d is the distance between adjacent receiving antennas, and λ is the wavelength corresponding to the initial frequency of the transmitted wave.
S4: calculating the relative speed of the target group and the normal direction of the vehicle to be measured by using the known radar mounting angle; the radar installation angle refers to a deflection angle of the center of a main beam of a radar relative to a normal direction of a vehicle in a horizontal plane in a vehicle body coordinate system;
in this step, the following formula is used for calculating the relative speed of the target and the normal direction of the vehicle to be measured:
wherein: vrTarget radial velocity measured for radar, theta target azimuth measured for radar, β radar mounting angle.
S5: counting the speed information of the target group by using a speed discrimination algorithm, setting a speed error threshold Verror to classify the targets, screening moving targets, and performing confidence matching to obtain target group results with the number of the targets ranked first three;
in this step, the step of screening out the moving target is: firstly, sorting the speed results of the detected targets; then, clustering the detection speed results according to a speed error threshold Verror, dividing the speed with the speed error within the Verror into a cluster until all the detection speeds are clustered, and carrying out mean value estimation on the detection speed results of each cluster; and finally, carrying out confidence degree classification according to the number of the targets in each cluster, wherein the more the number of the targets is, the higher the confidence degree of the speed estimation of the cluster is. Here, the confidence level indicates the degree to which the velocity estimation result is believed.
S6: limiting the speed range of the target measured by the radar, and removing target groups exceeding the speed boundary;
s7: preliminarily obtaining the estimation result of the speed of the current frame by using a confidence coefficient discrimination and multi-frame data association algorithm;
in this step, the step of estimating the speed of the vehicle is: firstly, the confidence coefficient is preferentially judged, and the speed mean value estimation result with the confidence coefficient higher than a certain threshold value can be directly output as the speed estimation result of the vehicle; secondly, judging the condition that the confidence coefficient judgment condition is not met by using a multi-frame data association method, wherein the judgment method is to perform threshold association with the vehicle speed results of the last two frames, the vehicle speed results meeting the threshold requirement are regarded as successful in association, and a group with the minimum association error is selected as the vehicle speed estimation result to be output; and when the two conditions are not met, selecting the average speed result with the largest number in the target cluster as the vehicle speed estimation result to output.
S8: and performing smooth filtering processing on the adjacent multi-frame own vehicle speed estimation result to finally determine the own vehicle speed estimation result.
In the step, the smooth filtering process sets a sliding window with a fixed length, and the sliding window slides backwards along with the delay of the number of frames, so that data in the window is filtered smoothly, and the estimation error of the speed of the vehicle can be reduced.
As shown in fig. 2, the result of the millimeter wave radar own vehicle speed estimation, and the OBD output vehicle speed are compared. The vehicle-mounted radar vehicle speed estimation method outputs the vehicle speed in real time, and the OBD outputs the vehicle speed at certain time intervals; compared with the OBD output vehicle speed, the millimeter wave radar has high estimation result precision and error less than 0.4 m/s. In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A vehicle speed estimation method based on a vehicle-mounted millimeter wave radar is characterized by comprising the following steps:
s1: the vehicle to be tested runs stably along the highway at any speed, and a periodic FMCW waveform is transmitted by a 77GHz vehicle-mounted high-precision millimeter wave radar;
s2: the radar receives an echo signal and performs two-dimensional FFT on single-frame echo data to obtain an R-D spectrum;
s3: solving the distance, the radial speed and the azimuth angle of each target;
s4: calculating the relative speed of the target and the normal direction of the vehicle to be measured:
wherein, VrAnd theta is the target radial speed and the target azimuth angle obtained in the step S3 respectively, β is the radar installation angle;
s5: counting the speed information of the target group by using a speed discrimination algorithm, setting a speed error threshold Verror to classify the targets, screening moving targets, and performing confidence matching to obtain target group results with the number of the targets ranked first three;
firstly, sorting the targets according to the target speed;
secondly, dividing the target speed with the speed error within the Verror into a cluster based on a set speed error threshold Verror;
thirdly, estimating the average value of the speed in each cluster of targets;
finally, setting a speed estimation confidence coefficient according to the number of targets in each cluster, wherein the more the number of the targets is, the higher the speed estimation confidence coefficient of the cluster is; reserving clusters with the number of targets ranked first three in the clusters;
s6: according to a set radar measuring target speed range, removing target clusters exceeding a speed boundary in clusters with the number of targets ranked in the first three;
s7: estimating the speed of the bicycle:
firstly, judging: whether a target cluster with the speed estimation confidence coefficient higher than a set threshold exists in the target clusters, if so, directly outputting the speed mean value estimation result of the target cluster meeting the conditions as the speed estimation result of the vehicle;
if the target clusters which meet the conditions do not exist, calculating the error between the current speed estimation value of each target cluster and the estimated speed of the vehicle in the previous two frames, and outputting the speed average value of the target cluster with the minimum error as the estimation result of the speed of the vehicle;
and when the situations are not met, selecting the average speed of the target cluster with the maximum number of targets as a vehicle speed estimation result for output.
In the step, the smooth filtering process sets a sliding window with a fixed length, and the sliding window slides backwards along with the delay of the number of frames, so that data in the window is filtered smoothly, and the estimation error of the speed of the vehicle can be reduced.
2. The method for estimating the own vehicle speed based on the vehicle-mounted millimeter wave radar as claimed in claim 1, wherein after the estimation of the own vehicle speed of multiple frames is completed, the estimation result of the own vehicle speed of adjacent multiple frames is subjected to smoothing filtering processing, and the estimation result of the own vehicle speed is finally determined.
3. The method as claimed in claim 1, wherein in step S3, the speed of the target is calculated according to the doppler dimension of the R-D spectrum, and the formula is as follows:
wherein, TcThe time interval of adjacent transmitted wave signals is defined, lambda is the wavelength corresponding to the initial frequency of the transmitted wave, and omega is the phase interval of two continuous transmitted wave signals;
the angle of the target is calculated according to the following formula:
where Δ d is the distance between adjacent receiving antennas, and λ is the wavelength corresponding to the initial frequency of the transmitted wave.
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| CN111751817A (en) * | 2020-07-21 | 2020-10-09 | 成都阶跃时进科技有限公司 | Vehicle driving parameter measuring device and method |
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| CN113256990A (en) * | 2021-07-13 | 2021-08-13 | 北京戍宁信息技术有限公司 | Method and system for collecting road vehicle information by radar based on clustering algorithm |
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