CN110412609B - Multi-pulse laser radar target detection method - Google Patents
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Abstract
The invention relates to the technical field of target detection of laser radars, in particular to a target detection and track search method of a multi-pulse laser radar based on target speed amplitude characteristics. Aiming at the problem of huge operation amount in the application of multi-pulse laser radar for detecting flight target tracks, based on the characteristic of target speed amplitude, by utilizing the characteristic that a large number of noise points irrelevant to a detected target exist in echo observation data of the multi-pulse laser radar, the echo observation data are firstly partitioned, binaryzation is carried out according to a set threshold value, all points with the median value of 1 are used as sample points to obtain a coordinate matrix M of the sample points, Hough transformation is carried out on the matrix M to obtain a matrix R, space parameters are voted on the R to obtain a voting table, the voting table is searched, the number of votes obtained in a parameter space is counted to obtain parameter space gathering points, and finally Hough inverse transformation is carried out on coordinate values of the parameter space gathering points to obtain potential target track straight lines of the echo observation data. The invention can realize the detection of the target linear track under the dense clutter background and has high operation speed.
Description
Technical Field
The invention relates to the technical field of target detection of laser radars, in particular to a target detection and track search method of a multi-pulse laser radar based on target speed amplitude characteristics, which is particularly suitable for quickly completing the tasks of target detection and track search under the environment with low signal-to-noise ratio.
Background
When a weak target is detected, the single-pulse laser radar can have detection difficulty, and the multi-pulse laser radar can utilize time domain multi-frame echo data to perform non-coherent accumulation so as to improve the signal-to-noise ratio and the detection probability. The method of tracking first and then detecting (TBD) tracks a target track with the largest energy accumulation from multiple frames of original observation data, and then determines the detection of the target, which can further improve the capability of the multi-pulse laser radar to detect a weak target, and the detection flow is shown in fig. 1. In the target track retrieval process based on the multi-pulse laser radar, a Hough transformation method is generally adopted. However, the multi-pulse laser radar has large observation data, and the TBD-based Hough transformation track detection method has the defects of huge calculation amount and difficulty in real-time implementation.
For the above problems, some improved Hough transform algorithms exist, such as the one in the literature (zhuyou, zhao wei, huangsong ling, et al. a straight line fast detection algorithm [ J ] instrument and meter bulletin, 2010,31(12):2774 and 2780.) based on improved Hough transform, which detects straight lines by detecting adjacent pixel points and clustering and then grouping. Li xiao et al propose in the literature (li xiao, sho xie, corner, et al. pre-detection tracking algorithm [ J ] based on modified Hough transform, modern defense techniques, 2016,44 (5)), to reduce false traces by fusion of local peaks upon non-coherent accumulation. The methods do not effectively utilize the characteristics of detection data and reduce the calculation amount of Hough transformation detection tracks. Aiming at the problem of huge operation amount in the application of detecting the flight target track by the multi-pulse laser radar, the method for reducing the noise points to participate in the mapping operation in Hough transformation as much as possible is adopted based on the target speed amplitude characteristic, so that the operation amount and the detection time are reduced, and the detection efficiency is improved.
Disclosure of Invention
The application provides a multi-pulse laser radar target detection method, wherein the radar transmits a pulse width T within a single pulse train repetition period TpP number of laser pulses, the radial velocity v of the radar target being in the range [ vmin,vmax]The method comprises the steps of:
s100, partitioning echo observation data of the multi-pulse laser radar according to a distance R, namely taking data corresponding to each R length units as a partition, and searching a target track, wherein R (| Tv)min|+|Tvmax|)；
S200, carrying out binarization processing on the partitioned data according to a set threshold value C, and setting data points not less than the threshold value C as 1;
s300, selecting all data points with the value of 1 as sample points to obtain a coordinate matrix M of sample point pixels, performing Hough transformation on the M, mapping the sample point data to a Hough transformation parameter space, and expressing the Hough transformation parameter space by using a matrix T (HM), wherein H is a transformation matrix of the Hough transformation, and the parameter space is a polar coordinate system theta-rho;
s400, calculating an accumulator table A (rho, theta) for T by adopting a voting method;
s500, searching an accumulator table A (rho, theta), counting the number of votes in the accumulator table A, and solving a maximum value point as an aggregation point of a parameter space;
s600, carrying out Hough inverse transformation on the coordinate values of the parameter space gathering points to obtain the potential target track straight line of the echo observation data.
Further, before the blocking in S100, there is a step S000: and compressing the sampled radar echo observation data, namely accumulating the values of every N points to be used as a point value of compressed data. Wherein the preferable value of N is 3-6.
Further, after S200, step S201 is also included: the binarized image data is subjected to a shift multiplication operation using a template of l × m size. The optimal movement is to move m columns to the rightmost end of the image from left to right, and then move l rows to the bottommost end from top to bottom. The optimal template has the following elements: when the image data in the template only has 1 point with the value of 1, the elements in the template are all zero; otherwise, the element of the point with the maximum amplitude value in the original data corresponding to the template coverage image data is taken as 1, and other elements are taken as zero. Wherein l is 2 or 3, and m is 2 or 3.
Further, the set threshold value is optimally
Wherein k is a threshold adjustment coefficient, P is the number of data points of the current block, PiThe value of the data point representing the current block. The preferred threshold adjustment factor range is 0 < k < 1.
Further, the voting method in step S400 to find the accumulator table means: the range of theta in the theta-rho of the polar coordinate system is [0,180 DEG ]]Is divided into according to the interval delta theta
And (4) quantizing the elements in the T by delta rho, carrying out parameter space voting, and calculating an accumulator table A (rho, theta).
Through analysis and simulation comparison, the method can realize the target linear track detection under the dense clutter background, reduces the algorithm operation amount, has high operation speed and has better algorithm performance than the algorithm of the reference comparison file.
Drawings
FIG. 1 is a TBD-based multi-pulse laser radar target detection process;
FIG. 2 illustrates method steps of the first embodiment;
FIG. 3 mapping of image space to parameter space;
FIG. 4 point-sinusoid dual transform;
FIG. 5 illustrates method steps of embodiment two;
FIG. 6 method steps of embodiment three;
FIG. 7 illustrates the detection results of the method of the present invention for both the echo signal of the target with narrow pulses and the clutter background;
FIG. 8 compares the performance of the inventive method with the comparison file algorithm.
Detailed Description
The target detection and track search method of the present invention is further explained below with reference to the accompanying drawings.
An embodiment of the present invention is shown in FIG. 2. A multi-pulse laser radar target detection method is disclosed, wherein the radar transmits a pulse width T within a single pulse train repetition period TpP number of laser pulses, the radial velocity v of the radar target being in the range [ vmin,vmax]The method comprises the steps of:
s100, partitioning echo observation data of the multi-pulse laser radar according to a distance R, namely taking data corresponding to each R length units as a partition, and searching a target track, wherein R (| Tv)min|+|Tvmax|)；
S200, carrying out binarization processing on the partitioned data according to a set threshold value C, and setting data points not less than the threshold value C as 1;
s300, selecting all data points with the value of 1 as sample points to obtain a coordinate matrix M of sample point pixels, performing Hough transformation on the M, mapping the sample point data to a Hough transformation parameter space, and expressing the Hough transformation parameter space by using a matrix T (HM), wherein H is a transformation matrix of the Hough transformation, and the parameter space is a polar coordinate system theta-rho;
specifically, assume the coordinate matrix is:
and (5) obtaining a conversion matrix H of Hough conversion according to the step S. Wherein, theta belongs to [0, pi ], S is pi/delta theta, and delta theta is the step length of theta in the parameter space. Let Δ θ be 1 ° and N be 180 °.
The point matrix T mapped into the parameter space is:
s400, calculating an accumulator table A (rho, theta) for T by adopting a voting method;
s500, searching an accumulator table A (rho, theta), counting the number of votes in the accumulator table A, and solving a maximum value point as an aggregation point of a parameter space;
s600, carrying out Hough inverse transformation on the coordinate values of the parameter space gathering points to obtain the potential target track straight line of the echo observation data. If the number of points on the target track is judged to be more than 5, the points are directly used as the target track judgment output; otherwise (or more than two tracks are more than 5), the above steps are repeated to track the tracks, the tracks are matched and checked with the previous tracks after the tracks are obtained in the next pulse train period, and the real target tracks are determined. And calculating a track slope according to the track coordinate values, calculating a current target distance value by combining the coordinates of the track pixel points and outputting the current target distance value.
Wherein the set threshold in step S200 is optimally
Wherein k is a threshold adjustment coefficient, P is the number of data points of the current block, PiThe value of the data point representing the current block. The preferred threshold adjustment factor range is 0 < k < 1.
In the Hough transform of step S300, the process of mapping the image space to the parameter space is as shown in FIG. 3Shown in the figure. As can be seen from the figure, there is a point-line duality of the X-Y coordinate system (image space) and the k-b coordinate system (parameter space), i.e., P in the image space1、P2The points respectively correspond to straight lines L in the parameter space1、L2. Intersection point P of two lines in parameter space0Which in turn corresponds to a two-point P in image space1、P2The determined straight line L0. Thus, a focus point P is detected in the parameter space0The presence of a straight line in the image can be confirmed.
Since the slope K of the vertical line in cartesian X-Y coordinates is infinite and thus inconvenient for calculation, a point-sinusoid dual transform is usually used to solve this problem. The point (X, Y) in the rectangular coordinates X-Y is transformed into a sinusoid in the polar coordinates theta-rho by point-sinusoid dual transformation, theta being taken (0-180 deg.).
ρ＝x cosθ+y sinθ，0≤θ≤π
It can be shown that a point on a straight line in the rectangular coordinates X-Y corresponds to an aggregation point of a cluster of sinusoids on the plane of the polar coordinate system θ - ρ after Hough transformation, as shown in fig. 4.
The voting method in step S400 to find the accumulator table means: the range of theta in the theta-rho of the polar coordinate system is [0,180 DEG ]]Is divided into according to the interval delta theta
And (4) quantizing the elements in the T by delta rho, carrying out parameter space voting, and calculating an accumulator table A (rho, theta).
Fig. 5 shows a second embodiment of the present invention. Wherein, step S000 means: and compressing the sampled radar echo observation data, namely accumulating the values of every N points to be used as a point value of compressed data. Wherein the preferable value of N is 3-6. Steps S100 to S600 are the same as in the first embodiment.
Fig. 6 shows an embodiment of the present invention. Wherein, step S201 refers to: the binarized image data is subjected to a shift multiplication operation using a template of l × m size. The optimal movement is to move m columns to the rightmost end of the image from left to right, and then move l rows to the bottommost end from top to bottom. The optimal template has the following elements: when the image data in the template only has 1 point with the value of 1, the elements in the template are all zero; otherwise, the element of the point with the maximum amplitude value in the original data corresponding to the template coverage image data is taken as 1, and other elements are taken as zero. Among them, preferred is l ═ 2 or 3, and m ═ 2 or 3. Steps S100 to S600 are the same as in the first embodiment, and step S000 is the same as in the second embodiment.
FIG. 7 is a simulation result of the present invention for detection of both narrow-pulse target echo signals and clutter background. Wherein, (a) is the range profile after detecting the echo observation data binarization in the step S200, and (b) is the detection result without the step S600.
FIG. 8 is a comparison of the performance of the algorithm of the present invention against a comparative reference (Lidao Smart, Wikipedia, Chaijiang, et al. Pre-detection tracking algorithm based on the modified Hough transform [ J ] modern defense techniques, 2016,44(5).) in the context of a uniform clutter background at a target speed of 0 m/s. IHT-TBD is the detection performance of the reference method, and SAS-HT-TBD is the method of the application. The SNR refers to the direct ratio of the amplitude peak value of the photoelectric detection target voltage signal to the root mean square of the noise, and the conversion relation between the non-logarithmic SNR and the logarithmic SNR taking dB as a unit is 20 logSNR. Therefore, the target detection and track search algorithm of the application is superior to the detection performance of the reference documents.
Claims (9)
1. A multi-pulse laser radar target detection method is disclosed, wherein the radar transmits a pulse width T within a single pulse train repetition period TpP number of laser pulses, the radial velocity v of the radar target being in the range [ vmin,vmax]The method comprises the steps of:
s100, partitioning echo observation data of the multi-pulse laser radar according to a distance R, namely, taking data corresponding to each R length units as a partition, and searching a target track, wherein R (| Tv)min|+|Tvmax|)；
S200, carrying out binarization processing on the partitioned data according to a set threshold value C, and setting data points not less than the threshold value C as 1;
s300, selecting all data points with the value of 1 as sample points to obtain a coordinate matrix M of sample point pixels, performing Hough transformation on the M, mapping the sample point data to a Hough transformation parameter space, and expressing the Hough transformation parameter space by using a matrix T (HM), wherein H is a transformation matrix of the Hough transformation, and the parameter space is a polar coordinate system theta-rho, theta is an angle parameter of the polar coordinate system, and rho is a length parameter of the polar coordinate system;
s400, calculating an accumulator table A (rho, theta) for T by adopting a voting method, wherein the calculation of the accumulator table for T by adopting the voting method refers to: the range of theta in the theta-rho of the polar coordinate system is [0,180 DEG ]]Is divided into according to the interval delta theta
The discrete cells quantize the elements in the T by delta rho, carry out parameter space voting, and calculate an accumulator table A (rho, theta);
s500, searching an accumulator table A (rho, theta), counting the number of votes in the accumulator table A, and solving a maximum value point as an aggregation point of a parameter space;
s600, Hough inverse transformation is carried out on the coordinate values of the parameter space gathering points to obtain the potential target track straight line of the echo observation data.
2. The method for multi-pulse lidar target detection of claim 1, further comprising, prior to the partitioning in S100, the step of S000: and compressing the sampled radar echo observation data, namely accumulating the values of every N points to be used as a point value of compressed data.
3. The method for multi-pulse lidar target detection of claim 2, wherein a value of N is 3-6.
4. The multi-pulse lidar target detection method of claim 1, wherein the method further comprises, after S200, step S201: the binarized image data is subjected to a shift multiplication operation using a template of l × m size.
5. The method for detecting the target of the multi-pulse lidar of claim 4, wherein in the step S201, the m columns are moved from left to right to the rightmost end of the image, and the l rows are moved from top to bottom to the bottommost end.
6. The multi-pulse lidar target detection method of claim 4, wherein values of elements in the template are: when the image data in the template only has 1 point with the value of 1, the elements in the template are all zero; otherwise, the element of the point with the maximum amplitude value in the original data corresponding to the template coverage image data is taken as 1, and other elements are taken as zero.
7. The multi-pulse lidar target detection method of claim 4, wherein l 2 or 3, m 2 or 3.
8. The multi-pulse lidar target detection method of claim 1, wherein the set threshold value is
Wherein k is a threshold adjustment coefficient, P is the number of data points of the current block, PiThe value of the data point representing the current block.
9. The multi-pulse lidar target detection method of claim 8, wherein the threshold adjustment factor 0 < k < 1.
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| CN103777187A (en) * | 2014-01-15 | 2014-05-07 | 杭州电子科技大学 | Weak target track-before-detect method based on traversal random Hough conversion |
| CN109581322A (en) * | 2018-09-10 | 2019-04-05 | 李涛 | A kind of MIMO radar target identification method under cheating interference based on Hough transform |
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