CN114428233B - Radar target track detection method based on multiple time and multiple resolutions - Google Patents
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- 230000004927 fusion Effects 0.000 claims abstract description 16
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- 238000002474 experimental method Methods 0.000 description 4
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Classifications
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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
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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
- G01S7/411—Identification of targets based on measurements of radar reflectivity
- G01S7/412—Identification of targets based on measurements of radar reflectivity based on a comparison between measured values and known or stored values
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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
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a radar target track detection method based on multiple time and multiple resolutions, which mainly solves the problems that in the prior art, the number of points is small, the number of false alarm points is large, the target motion track is difficult to detect, the uncertainty of the number and the spatial distribution of the target points is high, and all targets cannot be completely detected. The implementation scheme is as follows: receiving target point traces, rotating all the target point traces and projecting; finding out an optimal track section from the projection graph; obtaining track segment sets under different time windows and resolutions through multi-resolution multi-time detection of multiple time windows; matching the track segment in each time window with the track segment of the first time window; and fusing the track segments with the distance smaller than the fusion threshold in all the paired track segments to obtain a fused complete track. The invention can not only completely detect the targets under the conditions of dense targets, less measured values and different track numbers and distributions, but also completely detect the turning maneuver target tracks, and can be used for target identification.
Description
Technical Field
The invention belongs to the technical field of radars, and further relates to a radar multi-category target track detection method which can be used for target identification.
Background
With the rapid development of modern radars, several new problems arise with target detection. Firstly, when the radar resolution is larger than the target size or the target has a plurality of reflection points, a plurality of points are generated by one target; secondly, the detection environment problem is that the number of the false alarm points generated by clutter is usually smaller than that of the target points, and the target motion track is difficult to detect under the conditions of smaller number of the points and more false alarm points; third, in the case of dense target areas, such as detection areas near highways, airports, etc., where multiple targets are needed to be measured, it is very easy to detect near targets as a single target, thereby generating a merging of target trajectories.
The patent literature "a target track detection method" (patent application number: 2015100296575, application publication number: CN 104614716A) applied by the Shangshan Zhixian air technologies, inc. discloses a target track detection method, which comprises the steps of performing migration compensation on target echoes by using coherent accumulation, and further accumulating the target echoes by using a non-coherent accumulation method, so that effective accumulation of the target echoes is realized and the requirement on target detection is met. The method has the defect that the target motion track is difficult to detect under the condition that the false alarm points generated by clutter are more and the number of the target points is less.
The Chongqing university discloses a single-channel synthetic aperture radar SAR moving target detection method based on joint detection quantity in a patent document (patent application number: 2013101725615, application publication number: CN 103217677A) applied by Chongqing university. The method comprises the steps of obtaining sub-apertures corresponding to sub-images based on division of SAR images in azimuth spectrum, and correcting errors in amplitude and phase among different sub-images by combining a two-dimensional self-adaptive method; and then, the target detection is realized by utilizing the combined detection quantity of the second eigenvalue and the independent normalized phase obtained by the covariance matrix between the adjacent sub-images. According to the method, due to uncertainty of the number of target point traces and spatial distribution, a detection system is difficult to detect all targets completely through adjusting parameters.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a target track detection method based on multiple time and multiple resolutions so as to improve the detection rate of various targets under different parameters.
The technical idea of the invention is that a tracking idea before detection is utilized to detect multiple targets in different time-resolution windows, and then the obtained target track fragments are fused to obtain the complete track of the target.
According to the above thought, the implementation scheme of the invention comprises the following steps:
(1) Receiving, by the radar, the trace points in the detection area in real time in a set time period, wherein each trace point comprises one-dimensional time t i and two-dimensional space information (x i,yi), and forming a latest trace point set: { x i,yi,ti|i＝1,...,Ni,t1≤ti≤t2 }, where N i represents all the traces within the [ t 1,t2 ] time period;
(2) Detecting the target track segment:
(2a) Setting the target motion track in the time window to be approximate to a three-dimensional straight line, distributing the generated point tracks along the three-dimensional straight line, setting N v in total in the three-dimensional direction, and setting the target motion track to be close to one of N v three-dimensional vectors;
(2b) Rotating N i tracks according to N v vectors respectively, regarding each three-dimensional vector as a detection channel, and obtaining the rotated tracks (x i,yi,ti)
(2C) Dividing the x-y plane into N xNy grids with the width of delta xy, and tracing the rotated pointsProjecting to N xNy grids to obtain a projection diagram, wherein each trace falls into the corresponding grid/>, andThe method comprises the steps of adding 1 to the vote weight of a grid, finding out the grid with the largest vote number in N xNyNv cells from N v graphs of N v detection channels, wherein the point trace corresponding to the grid is the optimal track section in the current measured value set;
(2d) Taking the width delta xy of each grid as the image resolution, setting a detection threshold as H, taking out the optimal track segment, and comparing the optimal track segment with the detection threshold H:
If the optimal track section is greater than H, the optimal track section is a target track section, the point trace of the detected track section is removed from the point trace section set in the time window, and the operations (2 b) to (2 c) are repeated;
Otherwise, the track segment with the coincidence resolution of delta xy in the current time window is considered to be completely detected;
(2e) Taking the remaining trace after the target trace section is removed as the next step of input;
(2f) Detecting targets for multiple times by using multiple time windows with different time lengths and multiple resolutions with different grid widths, so as to obtain a target track segment set { M i |i=1, & gt, M }, wherein M is the total number of the time windows and the resolutions;
(3) Associating and fusing target track segments:
(3a) Setting a target track segment set M 1 obtained by the first time window-resolution detection as an existing track segment, and taking the target track segment sets obtained by the rest M-1 time window-resolution detection as new track segments;
(3b) Pairing one track segment in the first new track segment set with each track segment in the existing track segment set M 1 in a pairwise manner, finding out two track sets of the time overlapping part corresponding to the track of each pair of the track sets from each pair of the matching pairs, obtaining the distance s ab of the two sets by using an optimal sub-mode allocation algorithm, namely, obtaining the distance of the track pair, and finding out a pair of pairing modes with the minimum distance s ab from all the pairing modes, namely, successfully matching;
(3c) Repeating the operation (3 b) until the matching of all track segments in the first new track segment set is successfully stopped, and obtaining all matching successful pairs in the first new track segment set;
(3d) Let the fusion threshold be P, compare the distance s ab of each successful match pair with the fusion threshold P:
if s ab < P, updating the state of the existing track with the new track segment;
Otherwise, setting the new track segment as an existing track segment, performing (3 e);
(3e) Setting the threshold of the number of times of unsuccessful fusion as Q, and comparing the number of times of unsuccessful fusion of the existing track with the threshold of the number of times of unsuccessful fusion Q:
If it is greater than Q, the existing target trajectory is considered to have disappeared, setting it as a death trajectory;
Otherwise, adding one to the unfused times of the track, and fusing the unfused times with a target track segment set obtained by the residual time window-resolution;
(3f) Repeating the steps (3 b) to (3 e) m-2 times to obtain the complete target track after all the new track segments are fused.
Compared with the prior art, the invention has the following advantages:
Firstly, because the invention detects in different time-resolution windows when the radar system detects the target, and then fuses the obtained target track segments, the defects of the prior art that the number of false alarm points generated by clutter is less, the number of false alarm points is more and the target movement track is difficult to detect are overcome. The invention can detect for many times in a plurality of resolutions of a plurality of time windows, and accurately detect weak targets from the points containing more false alarm points.
Secondly, the method of multiple time and multiple resolution is utilized when the radar system detects various targets with different parameters in a complex environment, so that the defect that the detection system is difficult to detect all targets completely by adjusting parameters and cannot realize good detection of various targets with different parameters due to uncertainty of the number of target points and spatial distribution when the radar system detects the various targets in the prior art is overcome, and the method can detect various targets completely by multiple times in multiple resolutions of multiple time windows.
Drawings
FIG. 1 is a general flow chart of an implementation of the present invention;
FIG. 2 is a sub-flowchart of the detection of target track segments in the present invention;
FIG. 3 is a graph showing the effect of the present invention on the detection of a highway vehicle using millimeter wave radar;
fig. 4 shows the effect of the present invention on the detection of an aircraft using an air pulse radar.
Detailed Description
Embodiments and effects of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, the steps performed on the example are as follows:
And step 1, acquiring a target latest point trace to form a latest point trace set.
Setting a detection area, and receiving the point tracks of all targets in a set time period in the detection area in real time through a radar, wherein each point track comprises one-dimensional time t i and two-dimensional space information (x i,yi) to form a latest point track set: { x i,yi,ti }, where i=1, … N i,Ni denotes all the traces within the [ t 1,t2 ] period, t 1≤ti≤t2.
And 2, detecting a target track segment.
Referring to fig. 2, the specific implementation of this step is as follows:
(2.1) setting the target motion track in the time window to be approximate to a three-dimensional straight line, wherein the generated point tracks are distributed along the three-dimensional straight line, setting N v in total in the three-dimensional direction, and the target motion track is necessarily close to one of N v three-dimensional vectors;
(2.2) rotating the N i tracks respectively according to N v vectors, and regarding each three-dimensional vector as a detection channel to obtain a rotated track of each track (x i,yi,ti)
Wherein e x is the x-axis component of a three-dimensional vector, e y is the y-axis component of a three-dimensional vector, and e t is the time component of a three-dimensional vector;
(2.3) dividing the x-y plane into N xNy grids with the width of delta xy, and tracing the rotated points Projecting to N xNy grids to obtain a projection diagram, wherein each trace falls into the corresponding grid/>, andThe method comprises the steps of adding 1 to the vote weight of a grid, finding out the grid with the largest vote number in N xNyNv cells from N v graphs of N v detection channels, wherein the point trace corresponding to the grid is the optimal track section in the current measured value set;
(2.4) taking the width delta xy of each grid as the image resolution, setting the detection threshold as H, taking out the optimal track segment, and comparing the optimal track segment with the detection threshold H:
If the optimal track section is greater than H, the optimal track section is a target track section, the point trace of the detected track section is removed from the point trace section set in the time window, and the operations (2.2) to (2.3) are repeated;
Otherwise, the track segment with the resolution delta xy in the current time window is considered to be completely detected, and the process is executed (2.5);
(2.5) taking the rest of the trace points as the input of the next step;
(2.6) detecting the target multiple times using multiple time windows of different time lengths and multiple resolutions of different grid widths:
(2.6.1) setting a time window to have M gears from long to short and N gears from high to low, wherein M and N are equal to or greater than 2:
(2.6.2) repeating (2.1) to (2.4) in a short time window-high resolution manner, and then repeating (2.1) to (2.4) in a short time window-low resolution manner; repeating (2.1) to (2.4) according to a long time window-high resolution mode, repeating (2.1) to (2.4) according to a long time window-low resolution mode, taking the residual trace after the previous detection as the input of the next detection,
(2.6.3) Repeating (2.6.2) until all time windows and resolutions are used, stopping detection, and obtaining a target track segment set { M i |i=1 after each time window-resolution detection, & M }, wherein M is the total number of time window-resolutions.
And step3, associating and fusing the target track segments to obtain the complete track of the detection object.
(3.1) Setting a target track segment set M 1 obtained by the first time window-resolution detection as an existing track segment, and taking the target track segment sets obtained by the rest M-1 time window-resolution detection as new track segments;
(3.2) pairing one track segment in the first new track segment set with each track segment in the existing track segment set M 1 in pairs, finding out two track sets of the corresponding time overlapping part of the track from each pair of matching pairs, obtaining the distance s ab of the two sets by using an optimal sub-mode allocation algorithm, namely the distance of the track pair, and finding out a pair of pairing modes with the minimum distance s ab from all pairing modes, namely successful matching;
(3.3) repeating (3.2) until the matching is successfully stopped for all track segments in the first new track segment set, resulting in all matching successful pairs in the first new track segment set;
(3.4) setting the fusion threshold as P, and comparing the distance s ab of each successful matching pair with the fusion threshold P:
if s ab < P, updating the state of the existing track with the new track segment;
Otherwise, setting the new track segment as an existing track segment, executing (3.5);
(3.5) setting the threshold of the number of times of unsuccessful fusion as Q, and comparing the number of times of unsuccessful fusion of the existing track with the threshold of the number of times of unsuccessful fusion of Q:
If it is greater than Q, the existing target trajectory is considered to have disappeared, setting it as a death trajectory;
Otherwise, adding one to the unfused times of the track, and fusing the unfused times with a target track segment set obtained by the residual time window-resolution;
(3.6) repeating the steps (3.2) to (3.5) for m-2 times to obtain the complete target track after all new track segments are fused.
The effects of the present invention can be further described by the following experimental effects.
Conditions of the experiment:
and setting the measured value of the radar as a point trace with time space information, wherein the single frame detection rate of the radar in the received radar point trace is not lower than 50%, and the average measured distance error is not greater than the minimum distance between targets. The detected object motion has regularity such as linear motion or cornering maneuver with steering angle less than 15 ° per frame.
Second, content and result analysis of experiment:
experiment one, based on the experimental conditions, the invention adopts millimeter wave radar to detect the automobile running on the expressway, and the result is shown in figure 3. Wherein:
FIG. 3 (a) is a three-dimensional plot of an automobile entering a detection zone within 950 seconds, showing the presence of a large number of closely spaced, speed-varying objects;
FIG. 3 (b) shows that target track segments in different time windows are obtained by using a high-resolution grid for target track detection, and the target track segments in the point track comparison set are detected first;
FIG. 3 (c) is a target track segment of a target track detection by reusing the remaining tracks with a low resolution grid, where the track comparison discrete targets can be detected;
Fig. 3 (d) shows the complete target track segment obtained by fusing the track segments in the different time windows in fig. 3 (b) and fig. 3 (c), and it can be seen that most targets can be completely detected although the targets are dense and the number of points is different from the distribution.
Experiment two, based on the experimental conditions, the invention adopts an air radar to detect the aircraft with a circular area with the radius of 400km, and the result is shown in fig. 4. Wherein:
FIG. 4 (a) shows all the received traces in the detection area obtained by the radar, including part of turning targets, and part of targets have fewer measured values and are difficult to detect;
fig. 4 (b) shows the detection result at high resolution;
FIG. 4 (c) shows the detection result at low resolution;
Fig. 4 (d) shows the result after the track segments are fused, and it can be seen from fig. 4 (d) that although track segments are detected in the time window, the target track of the turning maneuver can be completely detected after the track segments are correlated and fused.
The experimental result shows that the invention can completely detect most targets under the conditions that the targets are dense and the number and the distribution of the spots are different; and the measured value is few, and the target track of turning maneuver can also be completely detected.
Claims (3)
1. The radar target track detection method based on multiple time and multiple resolutions is characterized by comprising the following steps of:
(1) Receiving, by the radar, the trace points in the detection area in real time in a set time period, wherein each trace point comprises one-dimensional time t i and two-dimensional space information (x i,yi), and forming a latest trace point set: { x i,yi,ti|i＝1,...,Ni,t1≤ti≤t2 }, where N i represents all the traces within the [ t 1,t2 ] time period;
(2) Detecting the target track segment:
(2a) Setting the target motion track in the time window to be approximate to a three-dimensional straight line, distributing the generated point tracks along the three-dimensional straight line, setting N v in total in the three-dimensional direction, and setting the target motion track to be close to one of N v three-dimensional vectors;
(2b) Rotating N i tracks according to N v vectors respectively, regarding each three-dimensional vector as a detection channel, and obtaining the rotated tracks (x i,yi,ti)
(2C) Dividing the x-y plane into N xNy grids with the width of delta xy, and tracing the rotated pointsProjecting to N xNy grids to obtain a projection diagram, wherein each trace falls into the corresponding grid/>, andThe method comprises the steps of adding 1 to the vote weight of a grid, finding out the grid with the largest vote number in N xNyNv cells from N v graphs of N v detection channels, wherein the point trace corresponding to the grid is the optimal track section in the current measured value set;
(2d) Taking the width delta xy of each grid as the image resolution, setting a detection threshold as H, taking out the optimal track segment, and comparing the optimal track segment with the detection threshold H:
If the optimal track section is greater than H, the optimal track section is a target track section, the point trace of the detected track section is removed from the point trace section set in the time window, and the operations (2 b) to (2 c) are repeated;
Otherwise, the track segment with the coincidence resolution of delta xy in the current time window is considered to be completely detected;
(2e) Taking the remaining trace after the target trace section is removed as the next step of input;
(2f) Detecting targets for multiple times by using multiple time windows with different time lengths and multiple resolutions with different grid widths, so as to obtain a target track segment set { M i |i=1, & gt, M }, wherein M is the total number of the time windows and the resolutions;
(3) Associating and fusing target track segments:
(3a) Setting a target track segment set M 1 obtained by the first time window-resolution detection as an existing track segment, and taking the target track segment sets obtained by the rest M-1 time window-resolution detection as new track segments;
(3b) Pairing one track segment in the first new track segment set with each track segment in the existing track segment set M 1 in a pairwise manner, finding out two track sets of the time overlapping part corresponding to the track of each pair of the track sets from each pair of the matching pairs, obtaining the distance s ab of the two sets by using an optimal sub-mode allocation algorithm, namely, obtaining the distance of the track pair, and finding out a pair of pairing modes with the minimum distance s ab from all the pairing modes, namely, successfully matching;
(3c) Repeating the operation (3 b) until the matching of all track segments in the first new track segment set is successfully stopped, and obtaining all matching successful pairs in the first new track segment set;
(3d) Let the fusion threshold be P, compare the distance s ab of each successful match pair with the fusion threshold P:
if s ab < P, updating the state of the existing track with the new track segment;
Otherwise, setting the new track segment as an existing track segment, performing (3 e);
(3e) Setting the threshold of the number of times of unsuccessful fusion as Q, and comparing the number of times of unsuccessful fusion of the existing track with the threshold of the number of times of unsuccessful fusion Q:
If it is greater than Q, the existing target trajectory is considered to have disappeared, setting it as a death trajectory;
Otherwise, adding one to the unfused times of the track, and fusing the unfused times with a target track segment set obtained by the residual time window-resolution;
(3f) Repeating the steps (3 b) to (3 e) m-2 times to obtain the complete target track after all the new track segments are fused.
2. The method of claim 1, wherein the rotated trace of each trace (x i,yi,ti) is obtained in (2 b)The expression is as follows:
Where e x is the x-axis component of a three-dimensional vector, e y is the y-axis component of a three-dimensional vector, and e t is the time component of a three-dimensional vector.
3. The method of claim 1, wherein (2 f) detecting the target multiple times using multiple time windows of different time lengths and multiple resolutions of different grid widths is accomplished by:
(2f1) Setting a time window to have M gears from long to short and N gears from high to low, wherein M and N are equal to or greater than 2:
(2f2) Repeating (2 a) to (2 d) in a short time window-high resolution manner, and repeating (2 a) to (2 d) in a short time window-low resolution manner; repeating the operations (2 a) to (2 d) in a long-time window-high resolution mode, repeating the operations (2 a) to (2 d) in a long-time window-low resolution mode, wherein each operation takes the residual trace after the previous detection as the input of the next detection,
(2F3) Repeating (2 f 2) until all the time windows and resolutions are used, and stopping detection to obtain a target track segment set { M i |i=1 after each time window-resolution detection, wherein M is the total number of the time windows-resolutions.
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| "A target detection and tracking method for multiple radar systems";Bo Yan等;《IEEE transactions on geoscience and remote sensing》;20220615;第1-21页 * |
| 基于满意度门限检测的多雷达航迹对提取研究;张大海;刘向阳;李华;;舰船电子工程;20110720(07);全文 * |
| 多雷达***数据融合与航迹预测;吴显亮;官慧峰;尹良泽;;数学的实践与认识;20100808(15);全文 * |
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