CN113269242B - Target detection trace aggregation method based on peak clustering - Google Patents

Target detection trace aggregation method based on peak clustering Download PDF

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CN113269242B
CN113269242B CN202110538477.5A CN202110538477A CN113269242B CN 113269242 B CN113269242 B CN 113269242B CN 202110538477 A CN202110538477 A CN 202110538477A CN 113269242 B CN113269242 B CN 113269242B
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CN113269242A (en
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曹建蜀
陈江宁
陈岁新
于昕凝
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University of Electronic Science and Technology of China
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    • 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
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Abstract

The invention discloses a target detection trace aggregation method based on peak value clustering, and relates to the technical field of signal and information processing. The algorithm is based on a trace point agglomeration algorithm for automatically searching peak value clustering of peak value data points, Euclidean distances of all collected data are calculated according to distance-Doppler two-dimensional information by sampling trace point data of CFAR detection results, peak value data points of each category are searched by using amplitude information for clustering, the difficulty that a plurality of targets which are adjacent to each other and have larger difference in trace point data amplitude and density distribution are difficult to correctly classify can be overcome, and the trace point agglomeration process can be completed at a higher speed on the basis of not increasing hardware structures and not increasing expenses.

Description

Target detection trace aggregation method based on peak clustering
Technical Field
The invention belongs to the technical field of signal and information processing, and particularly relates to a target detection trace aggregation method based on peak value clustering.
Background
As shown in fig. 1, which is a typical radar signal processing flow, a main task of a radar system is to determine whether a target exists, and to accurately detect and track multiple batches of targets, and as the radar action range increases and the radar resolution precision improves, the amount of trace point data collected by a radar increases, and a trace point expansion phenomenon occurs in the same target. The radar signal processing flow comprises a plurality of links, and after Constant False Alarm Rate (CFAR) processing, the CFAR detection traces need to be subjected to condensation processing.
In practical engineering application, trace aggregation is mainly divided into two steps, namely target division and target center extraction. Firstly, dividing a target: the amplitude of all traces affected by the same target is called a cluster of traces. And dividing all occupied traces into a plurality of different clusters, wherein each cluster represents an extended target. Secondly, extracting the target center: in the case where it is determined that all the traces are extracted, the center thereof is calculated for each cluster of traces. By means of point trace condensation, radar echo data can be compressed to the maximum extent, and detection parameter estimation is more accurate.
Currently, commonly used dot trace condensation algorithms such as a nine-dot method and a DBSCAN algorithm are generally performed as follows: firstly, carrying out amplitude threshold detection on data with the same distance and different Doppler dimensions, carrying out Doppler dimension condensation on traces passing a threshold, and recording Doppler dimension information of a target; then, carrying out depth search of a distance dimension on targets which are condensed in the Doppler dimension and have the same value in the Doppler dimension, carrying out distance dimension condensation, and judging effective targets according to a preset effective range of target width; and finally, estimating and recording the target parameters and outputting the result. However, due to the global parameters set by such a method, it is difficult to correctly classify a plurality of target data points with different density distributions and large amplitude differences at close distances, and although the modified DBSCAN dot trace condensation algorithm can solve the above problems, since the iteration times of the algorithm are too many and the operation speed is slow, it is a great challenge how to find a thought from a new direction to improve the dot trace condensation effect.
Disclosure of Invention
Aiming at the defects in the prior art, the target detection trace aggregation method based on peak value clustering solves the problems in the background technology.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a target detection trace agglomeration method based on peak value clustering comprises the following steps:
s1, collecting a plurality of CFAR detection trace data qi；
The CFAR detection trace data comprises distance dimensional data, Doppler dimensional data, azimuth angle data, pitch angle data and amplitude dimensional data;
s2, detecting trace data q for all CFARsiEstablishing an index and storing the index in a two-dimensional matrix M by using the indexK×LPerforming the following steps;
s3, according to the matrix MK×LAnd indexing the CFAR detected trace data qiAll peak point data p int；
S4, removing peak value data ptOuter trace data qiSorting according to the amplitude from large to small to obtain a data set OJ；
S5, calculating a data set OJEach trace data o injWith each peak point data ptAnd obtaining a distance matrix D formed by a plurality of Euclidean distancesJT；
S6, according to the distance matrix DJTSum peak point data ptFor data set OJClustering all CFAR detection trace data to obtain a plurality of trace classes;
and S7, performing condensation processing on each CFAR detection trace data in each trace point class to obtain mass center condensation points of each CFAR detection trace data in a distance dimension, a Doppler dimension, an azimuth dimension, a pitch angle dimension and an amplitude dimension, and realizing trace point condensation.
Further, in step S2, trace data q is detected for the CFARiThe formula for establishing the index is as follows:
mnew_i＝mi-mmin+2
nnew_i＝ni-nmin+2
in the formula, miAnd niDetecting traces q for ith CFAR respectivelyiI is more than or equal to 1 and less than or equal to N, and N is the total number of CFAR detection traces; m isminAnd nminRespectively detecting the minimum value of Doppler dimension and the minimum value of distance dimension of traces at all CFARs; m isnew_iAnd nnew_iAre each qiIn a two-dimensional matrix MK×LAn index value for the medium doppler dimension and the distance dimension;
the two-dimensional matrix MK×LValue M of the kth row and the l columnk,lComprises the following steps:
Figure BDA0003070674880000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003070674880000032
is indexed by (m)new_i,nnew_i) Detecting the amplitude dimension value of the trace by the CFAR;&representing a logical and operation.
The beneficial effects of the above further scheme are: the access speed of the target for detecting the trace data and the adjacent data thereof can be greatly accelerated.
Further, the step S3 is specifically:
s31, in the two-dimensional matrix MK×LIn (3), calculating the importance of each CFAR detection trace
Figure BDA0003070674880000033
And will be
Figure BDA0003070674880000034
The CFAR detection traces are used as each target core trace, namely the core CFAR detection traces;
s32 based on two-dimensional matrix MK×LDetecting traces by T core CFARs to form a peak point data set PT；
Wherein the peak point data set PTPeak point data p in (1)tI.e. detecting traces, p, for the core CFARt∈PT,1≤t≤T。
Further, in the step S31, importance is added
Figure BDA0003070674880000046
The calculation formula of (2) is as follows:
Figure BDA0003070674880000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003070674880000042
denotes the Doppler dimension index as mnew_iThe distance dimension index is nnew_iThe CFAR of (1) detects the importance of the trace;
Figure BDA0003070674880000043
denotes the Doppler dimension index as mnew_iThe distance dimension index is nnew_iDetecting the amplitude dimension value of the trace by the CFAR;
the expression of the function δ (·) is:
Figure BDA0003070674880000044
the beneficial effects of the above further scheme are: the number of targets can be directly obtained by directly searching the core detection traces of each target, so that the speed of target division in trace aggregation is greatly accelerated.
Further, in the step S5, the distance matrix DJTComprises the following steps:
Figure BDA0003070674880000045
wherein subscript J is data set OJThe number of trace data, subscript T as the number of peak data, and distance matrix DJTMiddle j row data DjTAs a data set OJMiddle j trace data ojRespectively forming distance vectors with Euclidean distances of T peak data;
the distance vector DjTComprises the following steps:
DjT＝[dj1,...djt...,djT]
in the formula (d)jtAs a data set OJMiddle j point trace data and peak point data set PTMiddle t peak detection trace ptT is more than or equal to 1 and less than or equal to T;
euclidean distance value djtComprises the following steps:
Figure BDA0003070674880000051
in the formula, mjAnd njAre respectively a data set OJDetecting trace Doppler dimension data values and distance dimension data values by the jth CFAR; m istAnd ntRespectively peak point data set PTMiddle t peak detection trace ptAnd T is more than or equal to 1 and less than or equal to T.
Further, the step S6 is specifically:
s61, distance matrix DJTDistance vector D injTArranging according to ascending order from small to large to obtain a set I of index vectorsTAnd according to ITRearranging peak point data set PTPeak point data p in (1)tTo obtain a new peak detection trace GkK is more than or equal to 1 and less than or equal to T, and T is the total number of detection traces of the core CFAR;
s62, calculating OJDetection trace of current jth CFAR and detection trace of peak value GkDetecting trace indexes (inten _ x, inten _ y) of adjacent CFARs in the same direction;
s63, when CFAR detects the trace index (inten _ x, inten _ y) corresponding CFAR detects the trace amplitude dimension value hinten_x,inten_yAnd index (m)new_j,nnew_j) Corresponding CFAR detection trace amplitude dimension value
Figure BDA0003070674880000052
Satisfy the requirement of
Figure BDA0003070674880000053
When it is in contact with OJThe jth CFAR detection trace is divided into peak detection trace GkTraces of the same category, i.e., on the same target;
s64, repeatedly executing the steps S62-63 within the range that k is more than or equal to 1 and less than or equal to T to obtain a plurality of trace point clusters;
at the same time, when OJWhen the jth CFAR detection trace cannot be classified into any category, the jth CFAR detection trace is classified into a noise trace.
Further, the calculation formula of the CFAR detection trace index (inten _ x, inten _ y) in step S62 is as follows:
Figure BDA0003070674880000054
Figure BDA0003070674880000061
in the formula, mnew_jAnd nnew_jAre each OJDetecting the Doppler dimension and the distance dimension index value of the trace in the jth CFAR; m isg_kAnd ng_kRespectively detecting the trace G for the peak valuekAnd the index value of the distance dimension.
The beneficial effects of the above further scheme are: the characteristic that the amplitude dimension data of the target detection trace in the adjacent space of the distance dimension and the Doppler dimension are aggregated to the core detection trace is fully utilized, the iteration times of target division of the non-core detection trace are greatly reduced, and therefore the speed of target division of the non-core detection trace is greatly increased.
Further, in step S7, the formula for performing the aggregation processing on the CFAR detection trace data is as follows:
Figure BDA0003070674880000062
Figure BDA0003070674880000063
Figure BDA0003070674880000064
Figure BDA0003070674880000065
in the formula, AvDetecting point condensation points of the CFAR detection point traces in the v point trace class at the mass center of the amplitude dimension; a. thevxDetecting the data value of the trace in the amplitude dimension for the xth CFAR in the v trace class; MAX {. denotes taking the maximum value; k is a radical ofvDetecting the total number of traces for CFAR in the v trace class; o isvDetecting a mass center condensation point of traces in an angle dimension for CFARs in a v-th trace class; o isvxDetecting the data value of the trace in the angle dimension for the xth CFAR in the v trace class; fvDetecting a point of mass center condensation in the doppler dimension for a CFAR in the v-th trace class; fvxDetecting data values of the traces in the Doppler dimension for the x CFAR in the v trace class; rvDetecting point condensation points of traces at the mass center of the distance dimension for CFARs in the v-th trace class; rvxData values in the distance dimension are detected for the xth CFAR in the xth trace class.
The beneficial effects of the above further scheme are: the multiple CFAR detection traces in the same category are condensed into one trace, the data volume is reduced, the data estimation results of the distance dimension, the Doppler dimension and the angle dimension are more accurate through the centroid algorithm, and meanwhile, the problem that a weak target is interfered by a strong target and is filtered can be solved by using the maximum value method for the amplitude dimension.
In conclusion, the beneficial effects of the invention are as follows:
(1) linear space indexes are established for all target point trace data by using distance dimension and Doppler dimension information, and all target detection point traces are stored locally by using the indexes, so that the access speed of the target detection point trace data and adjacent data thereof can be greatly accelerated;
(2) by searching peak data and taking the peak data as the core target detection trace data of each target, the iteration times can be reduced, so that the speed of target division is greatly accelerated, and a plurality of target trace data with different density distribution and larger amplitude difference can be correctly divided;
(3) the target detection trace data except the peak data is sequenced from large to small according to the amplitude dimension and then accessed, so that the weak target can not be interfered by the amplitude of the strong target, and therefore the weak target is correctly divided into different targets.
Drawings
FIG. 1 is a flow chart of typical radar signal processing in the background art provided by the present invention;
FIG. 2 is a CFAR detection trace data diagram of a group of targets before target segmentation processing in an embodiment of the present invention;
FIG. 3 is a flowchart of a target detection trace aggregation method based on peak clustering according to the present invention;
FIG. 4 is a top view of a target partition result in an embodiment provided by the present invention;
FIG. 5 is a three-dimensional diagram of the trace-point condensation results in the distance dimension, Doppler and amplitude dimensions in an embodiment provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 2, in order to detect trace data map in CFAR of the whole target before target division, 19 target distributions with different trace density distributions and different signal strengths can be directly observed from the map.
As shown in fig. 3, the method for detecting trace aggregation in a target based on peak clustering provided in this embodiment includes the following steps:
s1, collecting a plurality of CFAR detection trace data qi；
The CFAR detection trace data comprises distance dimensional data, Doppler dimensional data, azimuth angle data, pitch angle data and amplitude dimensional data;
s2, detecting trace data q for all CFARsiEstablishing an index and storing the index in a two-dimensional matrix M by using the indexK×LPerforming the following steps;
s3, according to the matrix MK×LAnd indexing the CFAR detected trace data qiAll peak point data p int；
S4, removing peak value data ptOuter trace data qiSorting according to the amplitude from large to small to obtain a data set OJ；
S5, calculating a data set OJEach trace data o injWith each peak point data ptAnd obtaining a distance matrix D formed by a plurality of Euclidean distancesJT；
S6, according to the distance matrix DJTSum peak point data ptFor data set OJClustering all CFAR detection trace data to obtain a plurality of trace classes;
and S7, performing condensation processing on each CFAR detection trace data in each trace point class to obtain mass center condensation points of each CFAR detection trace data in a distance dimension, a Doppler dimension, an azimuth dimension, a pitch angle dimension and an amplitude dimension, and realizing trace point condensation.
In step S1 of the present embodiment, CFAR detection trace data of 19 targets having density differences and amplitude differences is used as input data, and the 19 targets are as shown in fig. 2.
In step S2 of the present embodiment, trace data q is detected for CFARiThe formula for establishing the index is as follows:
mnew_i＝mi-mmin+2
nnew_i＝ni-nmin+2
in the formula, miAnd niDetecting traces q for ith CFAR respectivelyiI is more than or equal to 1 and less than or equal to N, and N is the total number of CFAR detection traces; m isminAnd nminRespectively detecting the minimum value of Doppler dimension and the minimum value of distance dimension of traces at all CFARs; m isnew_iAnd nnew_iAre each qiIn a two-dimensional matrix MK×LAn index value for the medium doppler dimension and the distance dimension;
the two-dimensional matrix MK×LValue M of the kth row and the l columnk,lComprises the following steps:
Figure BDA0003070674880000091
in the formula (I), the compound is shown in the specification,
Figure BDA0003070674880000092
is indexed by (m)new_i,nnew_i) Detecting the amplitude dimension value of the trace by the CFAR;&representing logical AND operations, i.e. symbols&When both conditions are true, the result of the symbol processing is true, and M is true under all other conditionsk,lAre all 0, including the case where no index is paired with (k, l).
Step S3 of this embodiment specifically includes:
s31, in the two-dimensional matrix MK×LIn (3), calculating the importance of each CFAR detection trace
Figure BDA0003070674880000093
And will be
Figure BDA0003070674880000094
CFAR (constant false alarm Rate) detection traceAs each target core trace, namely a core CFAR detection trace;
s32 based on two-dimensional matrix MK×LDetecting traces by T core CFARs to form a peak point data set PT；
Wherein the peak point data set PTPeak point data p in (1)tI.e. detecting traces, p, for the core CFARt∈PT,1≤t≤T。
In the above step S31, importance
Figure BDA0003070674880000095
The calculation formula of (2) is as follows:
Figure BDA0003070674880000101
in the formula (I), the compound is shown in the specification,
Figure BDA0003070674880000102
denotes the Doppler dimension index as mnew_iThe distance dimension index is nnew_iThe CFAR of (1) detects the importance of the trace;
Figure BDA0003070674880000103
denotes the Doppler dimension index as mnew_iThe distance dimension index is nnew_iDetecting the amplitude dimension value of the trace by the CFAR;
the expression of the function δ (·) is:
Figure BDA0003070674880000104
after the above treatment, the matrix is arranged in the matrix MK×LThe detection traces (namely peak data) of the T core CFARs are searched in total to form a peak point data set PT(wherein p ist∈PT,1≤t≤T)。
In step S5 of the present embodiment, the distance matrix DJTComprises the following steps:
Figure BDA0003070674880000105
wherein subscript J is data set OJThe number of trace data, subscript T as the number of peak data, and distance matrix DJTMiddle j row data DjTAs a data set OJMiddle j trace data ojRespectively forming distance vectors with Euclidean distances of T peak data;
distance vector DjTComprises the following steps:
DjT＝[dj1,...djt...,djT]
in the formula (d)jtAs a data set OJMiddle j point trace data and peak point data set PTMiddle t peak detection trace ptT is more than or equal to 1 and less than or equal to T;
euclidean distance value djtComprises the following steps:
Figure BDA0003070674880000111
in the formula, mjAnd njAre respectively a data set OJDetecting trace Doppler dimension data values and distance dimension data values by the jth CFAR; m istAnd ntRespectively peak point data set PTMiddle t peak detection trace ptAnd T is more than or equal to 1 and less than or equal to T.
Step S6 of this embodiment specifically includes:
s61, distance matrix DJTDistance vector D injTArranging according to ascending order from small to large to obtain a set I of index vectorsTAnd according to ITRearranging peak point data set PTPeak point data p in (1)tTo obtain a new peak detection trace GkK is more than or equal to 1 and less than or equal to T, and T is the total number of detection traces of the core CFAR;
s62, calculating OJDetection trace of current jth CFAR and detection trace of peak value GkSame-direction adjacent CFAR detection trace index(inten_x,inten_y)；
S63, when CFAR detects the trace index (inten _ x, inten _ y) corresponding CFAR detects the trace amplitude dimension value hinten_x,inten_yAnd index (m)new_j,nnew_j) Corresponding CFAR detection trace amplitude dimension value
Figure BDA0003070674880000112
Satisfy the requirement of
Figure BDA0003070674880000113
When it is in contact with OJThe jth CFAR detection trace is divided into peak detection trace GkTraces of the same category, i.e., on the same target;
s64, repeatedly executing the steps S62-63 within the range that k is more than or equal to 1 and less than or equal to T to obtain a plurality of trace point clusters;
at the same time, when OJWhen the jth CFAR detection trace cannot be classified into any category, the jth CFAR detection trace is classified into a noise trace.
The calculation formula of CFAR detection trace index (inten _ x, inten _ y) in step S62 is as follows:
Figure BDA0003070674880000114
Figure BDA0003070674880000115
in the formula, mnew_jAnd nnew_jAre each OJDetecting the Doppler dimension and the distance dimension index value of the trace in the jth CFAR; m isg_kAnd ng_kRespectively detecting the trace G for the peak valuekAnd the index value of the distance dimension.
In step S7 of this embodiment, the formula for performing the aggregation processing on the CFAR detection trace data is as follows:
Figure BDA0003070674880000121
Figure BDA0003070674880000122
Figure BDA0003070674880000123
Figure BDA0003070674880000124
in the formula, AvDetecting point condensation points of the CFAR detection point traces in the v point trace class at the mass center of the amplitude dimension; a. thevxDetecting the data value of the trace in the amplitude dimension for the xth CFAR in the v trace class; MAX {. denotes taking the maximum value; k is a radical ofvDetecting the total number of traces for CFAR in the v trace class; o isvDetecting a mass center condensation point of traces in an angle dimension for CFARs in a v-th trace class; o isvxDetecting the data value of the trace in the angle dimension for the xth CFAR in the v trace class; fvDetecting a point of mass center condensation in the doppler dimension for a CFAR in the v-th trace class; fvxDetecting data values of the traces in the Doppler dimension for the x CFAR in the v trace class; rvDetecting point condensation points of traces at the mass center of the distance dimension for CFARs in the v-th trace class; rvxData values in the distance dimension are detected for the xth CFAR in the xth trace class.
Based on the above trace point aggregation method, fig. 4 is a top view result of data in doppler dimension, distance dimension and doppler dimension after target division in the trace point aggregation algorithm of this embodiment, which greatly accelerates the access speed of a target detection trace and its neighboring target detection traces by using a spatial linear index, and greatly accelerates the target division process in trace point aggregation by searching the core trace point of each target, i.e., the peak trace point, by using an importance algorithm. The top view of the final target division result in the doppler dimension, the distance dimension and the doppler dimension is shown in fig. 4, and it is found by comparing fig. 2 that the algorithm correctly performs target division on the trace data. Fig. 5 is a three-dimensional result graph of data in doppler dimension, distance dimension and doppler dimension after the peak search point trace coagulation algorithm coagulation processing based on the search engine principle in this embodiment, and it is found by comparing fig. 2 that the algorithm reduces the number of detected point traces in each target, and reduces the problem that a weak target is filtered due to interference of a strong target.

Claims (5)

1. A target detection trace agglomeration method based on peak value clustering is characterized by comprising the following steps:
s1, collecting a plurality of CFAR detection trace data qi；
The CFAR detection trace data comprises distance dimensional data, Doppler dimensional data, azimuth angle data, pitch angle data and amplitude dimensional data;
s2, detecting trace data q for all CFARsiEstablishing an index and storing the index in a two-dimensional matrix M by using the indexK×LPerforming the following steps;
s3, according to the two-dimensional matrix MK×LAnd indexing the CFAR detected trace data qiAll peak point data p int；
S4, removing peak value data ptOuter trace data qiSorting according to the amplitude from large to small to obtain a data set OJ；
S5, calculating a data set OJEach trace data o injWith each peak point data ptAnd obtaining a distance matrix D formed by a plurality of Euclidean distancesJT；
S6, according to the distance matrix DJTSum peak point data ptFor data set OJClustering all CFAR detection trace data to obtain a plurality of trace classes;
s7, performing condensation processing on each CFAR detection trace data in each trace point class to obtain mass center condensation points of each CFAR detection trace data in distance dimension, Doppler dimension, azimuth dimension, pitch angle and amplitude dimension, and realizing trace point condensation;
in step S2, trace data q is detected for the CFARiBuilding cableThe formula quoted is:
mnew_i＝mi-mmin+2
nnew_i＝ni-nmin+2
in the formula, miAnd niDetecting traces q for ith CFAR respectivelyiI is more than or equal to 1 and less than or equal to N, and N is the total number of CFAR detection traces; m isminAnd nminRespectively detecting the minimum value of Doppler dimension and the minimum value of distance dimension of traces at all CFARs; m isnew_iAnd nnew_iAre each qiIn a two-dimensional matrix MK×LAn index value for the medium doppler dimension and the distance dimension;
the two-dimensional matrix MK×LValue M of the kth row and the l columnk,lComprises the following steps:
Figure FDA0003482686610000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003482686610000022
is indexed by (m)new_i,nnew_i) Detecting the amplitude dimension value of the trace by the CFAR;&representing a logical and operation;
the step S3 specifically includes:
s31, in the two-dimensional matrix MK×LIn (3), calculating the importance of each CFAR detection trace
Figure FDA0003482686610000023
And will be
Figure FDA0003482686610000024
The CFAR detection traces are used as each target core trace, namely the core CFAR detection traces;
s32 based on two-dimensional matrix MK×LDetecting traces by T core CFARs to form a peak point data set PT；
Wherein the peak point data set PTPeak point data p in (1)tI.e. detecting traces, p, for the core CFARt∈PT,1≤t≤T；
In the step S31, importance is added
Figure FDA0003482686610000025
The calculation formula of (2) is as follows:
Figure FDA0003482686610000026
in the formula (I), the compound is shown in the specification,
Figure FDA0003482686610000027
denotes the Doppler dimension index as mnew_iThe distance dimension index is nnew_iThe CFAR of (1) detects the importance of the trace;
Figure FDA0003482686610000028
denotes the Doppler dimension index as mnew_iThe distance dimension index is nnew_iDetecting the amplitude dimension value of the trace by the CFAR;
the expression of the function δ (·) is:
Figure FDA0003482686610000029
2. the method for target detection trace aggregation based on peak clustering as claimed in claim 1, wherein in step S5, the distance matrix D isJTComprises the following steps:
Figure FDA0003482686610000031
wherein subscript J is data set OJThe number of trace data, subscript T as the number of peak data, and distance matrix DJTMiddle j row data DjTAs a collection of dataOJMiddle j trace data ojRespectively forming distance vectors with Euclidean distances of T peak data;
the distance vector DjTComprises the following steps:
DjT＝[dj1,...djt...,djT]
in the formula (d)jtAs a data set OJMiddle j point trace data and peak point data set PTMiddle t peak detection trace ptT is more than or equal to 1 and less than or equal to T;
euclidean distance value djtComprises the following steps:
Figure FDA0003482686610000032
in the formula, mjAnd njAre respectively a data set OJDetecting trace Doppler dimension data values and distance dimension data values by the jth CFAR; m istAnd ntRespectively peak point data set PTMiddle t peak detection trace ptAnd T is more than or equal to 1 and less than or equal to T.
3. The method for target detection trace aggregation based on peak clustering as claimed in claim 2, wherein the step S6 specifically comprises:
s61, distance matrix DJTDistance vector D injTArranging according to ascending order from small to large to obtain a set I of index vectorsTAnd according to ITRearranging peak point data set PTPeak point data p in (1)tTo obtain a new peak detection trace GkK is more than or equal to 1 and less than or equal to T, and T is the total number of detection traces of the core CFAR;
s62, calculating OJDetection trace of current jth CFAR and detection trace of peak value GkDetecting trace indexes (inten _ x, inten _ y) of adjacent CFARs in the same direction;
s63, when CFAR detects the trace index (inten _ x, inten _ y) corresponding CFAR detects the trace amplitude dimension value hinten_x,inten_yAnd index (m)new_j,nnew_j) Corresponding CFAR detection trace amplitude dimension value
Figure FDA0003482686610000041
Satisfy the requirement of
Figure FDA0003482686610000042
When it is in contact with OJThe jth CFAR detection trace is divided into peak detection trace GkTraces of the same category, i.e., on the same target;
s64, repeatedly executing the steps S62-63 within the range that k is more than or equal to 1 and less than or equal to T to obtain a plurality of trace point clusters;
at the same time, when OJWhen the jth CFAR detection trace cannot be classified into any category, the jth CFAR detection trace is classified into a noise trace.
4. The method for target-detected trace agglomeration based on peak clustering according to claim 3, wherein the formula for CFAR detection of trace indices (inten _ x, inten _ y) in step S62 is as follows:
Figure FDA0003482686610000043
Figure FDA0003482686610000044
in the formula, mnew_jAnd nnew_jAre each OJDetecting the Doppler dimension and the distance dimension index value of the trace in the jth CFAR; m isg_kAnd ng_kRespectively detecting the trace G for the peak valuekAnd the index value of the distance dimension.
5. The method for clustering target detection traces based on peak clustering according to claim 3, wherein in step S7, the formula for clustering CFAR detection trace data is as follows:
Figure FDA0003482686610000045
Figure FDA0003482686610000046
Figure FDA0003482686610000047
Figure FDA0003482686610000048
in the formula, AvDetecting point condensation points of the CFAR detection point traces in the v point trace class at the mass center of the amplitude dimension; a. thevxDetecting the data value of the trace in the amplitude dimension for the xth CFAR in the v trace class; MAX {. denotes taking the maximum value; k is a radical ofvDetecting the total number of traces for CFAR in the v trace class; o isvDetecting a mass center condensation point of traces in an angle dimension for CFARs in a v-th trace class; o isvxDetecting the data value of the trace in the angle dimension for the xth CFAR in the v trace class; fvDetecting a point of mass center condensation in the doppler dimension for a CFAR in the v-th trace class; fvxDetecting data values of the traces in the Doppler dimension for the x CFAR in the v trace class; rvDetecting point condensation points of traces at the mass center of the distance dimension for CFARs in the v-th trace class; rvxData values in the distance dimension are detected for the xth CFAR in the xth trace class.
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