CN108345864B - Random set type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering - Google Patents

Random set type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering Download PDF

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CN108345864B
CN108345864B CN201810182612.5A CN201810182612A CN108345864B CN 108345864 B CN108345864 B CN 108345864B CN 201810182612 A CN201810182612 A CN 201810182612A CN 108345864 B CN108345864 B CN 108345864B
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徐欣
刘伟峰
张桂林
赵真一
饶佳人
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Abstract

The invention discloses a random aggregation type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering, which is characterized in that a similarity distance matrix is constructed aiming at signal parameter data of a random aggregation type airborne radar radiation source target, the similarity between every two random aggregation type measured values is compared, random aggregation type clustering is iteratively fused on the basis of a hierarchical clustering method on the basis of the similarity distance matrix until clusters meeting a random aggregation distance threshold cannot be found, the support degree and the random aggregation value of each new cluster are iteratively calculated at the same time, and finally, the random aggregation value corresponding to the clusters meeting the support degree threshold is mined and used as high-frequency mode output. The invention has the advantages that: (1) a similarity comparison method for random set type radar radiation source signal parameters is provided; (2) based on the similarity distance matrix, a high-frequency signal parameter mode can be further mined; (3) the calculation cost is low, and the implementation method is engineered.

Description

Random set type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering
Technical Field
The invention relates to the field of analysis and processing of random set type radar radiation source signal parameter data, in particular to a weighted clustering-based random set type radar radiation source signal parameter high-frequency mode mining method.
Background
As is known, the signal characteristics of radar radiation sources are increasing nowadays, and airborne radar radiation sources exhibit, in addition to the traditional type, a random set of signal parameters consisting of several random measured values. The conventional signal feature types are mostly continuous type and discrete type. Nowadays, random collective type signal characteristics also become an important type of radar radiation source data. This is because, as the complexity of the radar radiation source increases, the variety and form of the signal characteristics also becomes more and more diverse. Furthermore, due to the advancement of signal characteristic measurement techniques and the influence of the measurement environment (e.g., noise), the measured value of the signal characteristic is usually no longer a fixed value, but rather a set of several random values. The existing radar radiation source signal characteristic analysis method and the existing radar radiation source type identification method almost aim at the signal characteristics of fixed values, and cannot be specially used for describing and analyzing random set type signal characteristics. With the improvement of science and technology, the data resource of the random set type radar radiation source also increases, and a corresponding processing method is urgently needed.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a random set type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a random set type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering is characterized in that a similarity distance matrix is constructed according to signal parameter data of a random set type airborne radar radiation source target, similarity between two random set type measurement values is compared, random set type clustering is fused iteratively on the basis of a hierarchical clustering method on the basis of the similarity distance matrix until clusters meeting a random set distance threshold cannot be found, meanwhile, the support degree and the random set value of each new cluster are calculated iteratively, and finally, the random set value corresponding to the clusters meeting the support degree threshold is mined to serve as high-frequency mode output.
A random set type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering comprises the following steps:
(1) radar radiation source target according to random set size SORDSorting from big to small;
(2) initializing a similarity distance matrix with the size of n multiplied by n;
(3) setting a target index s of a current radar radiation source;
(4) setting a target index t of a radar radiation source to be compared;
(5) judging the target index to be compared, if t is not more than n and S (ORD [ t ])/S (ORD [ S ]) is more than or equal to n, continuing the step 6, and if t is more than or equal to n, continuing the step 8;
(6) updating the current target index value;
(7) judging the current target index, if s is more than or equal to n, continuing the step 10, otherwise, returning to the step 4;
(8) calculating a similarity distance dist between radar radiation sources and updating a similarity matrix;
(9) updating the radar radiation source index to be compared, and returning to the step 5;
(10) constructing n initial clusters;
(11) judging two clusters C with minimum similarity distance in distance matrixuAnd Cv(u<v) whether dist (C) is satisfiedu,Cv) If yes, continuing to step 12, if not, jumping to step 14;
(12) merged cluster CuAnd CvAs a new cluster CwAnd updating;
(13) updating the distance matrix based on the new cluster, defining the distance between the new cluster and other clusters as the minimum value of the distances between the original two clusters forming the new cluster and other clusters, and returning to the step 11;
(14) and finding out clusters with all support degrees meeting the threshold minsup, and outputting a corresponding random set as a high-frequency mode of the radar parameter.
Further, the random set distance threshold is used for comparing the similarity between the random measurement value sets, and the support threshold min is used for judging whether the signal parameter mode belongs to the high frequency.
Further, the measured value of the airborne radar radiation source signal parameter is a random set consisting of a plurality of random measured values.
Has the advantages that: the invention has the advantages that: (1) a similarity comparison method for random set type radar radiation source signal parameters is provided; (2) based on the similarity distance matrix, a high-frequency signal parameter mode can be further mined; (3) the calculation cost is low, and the implementation method is engineered.
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FIG. 1 is a flow chart of the operation of the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The method comprises the steps of constructing a similarity distance matrix aiming at signal parameter data of a random set type airborne radar radiation source target, comparing the similarity between every two random set type measured values, fusing random set type clusters in an iterative mode on the basis of a hierarchical clustering method on the basis of the similarity distance matrix until a cluster meeting a random set distance threshold cannot be found, calculating the support degree and the random set value of each new cluster in an iterative mode, and finally mining the random set value corresponding to the cluster meeting the support degree threshold as a high-frequency mode to be output.
Assume that the data set of airborne radar radiation source signal parameters consists of a number of airborne radar radiation source targets { o1, o2, …, on }, each of which consists of a random set of measurements of the signal parameters a (the measurements are a set consisting of a number of random numbers). In particular, a radar radiation source target oiThe measured values on the signal parameter a may be represented as a random set Si＝{Sip}pWherein S isipRepresenting an object oiOne of several random measurements on the signal parameter a. In addition, assume that the similarity threshold of the random measurement value is, the distance threshold of the random set is (between 0 and 1), the support threshold is minsup, and the minimum weight threshold of the measurement value is minw.
As shown in fig. 1, the method of the present invention specifically includes the following steps:
(1) target sorting;
sequencing radar radiation source targets from large to small according to the size of a random set, and recording as ORD (order of arrival) so as to enable S to beORD[1]≥SORD[2]≥…≥SORD[n]Wherein S isORD[1]To representThe random set size of the first radar source target in the ORD sequence, and so on.
(2) Initializing a similarity distance matrix;
the initialized similarity distance matrix DistAlrray has the size of n multiplied by n, the row index value corresponds to the index value of the ORD sequence, the diagonal line and the lower unit take values of null, and the upper triangular unit takes a value of infinity.
(3) Setting a current target;
and setting the current radar radiation source target index s to be 1.
(4) Setting a target to be compared;
and setting the target index t of the radar radiation source to be compared as s + 1.
(5) Judging a target to be compared;
and judging the target index value t to be compared, if t is not more than or equal to n and size (ORD [ t ])/size (ORD [ s ]) is more than or equal to n, continuing the step 6, and if t is more than or equal to n, continuing the step 8.
(6) Modifying the current target;
and updating the current target index value s to be s + 1.
(7) Judging a current target;
and if the current target index value s is larger than or equal to n, jumping to the step 10, otherwise, returning to the step 4.
(8) Calculating a similarity distance;
calculating the similarity distance between the radar radiation sources ORD [ s ] and ORD [ t ] and updating the similarity matrix DistAlrray, wherein the calculation method of the similarity distance value comprises the following steps:
Figure BDA0001589308450000031
wherein S isORD[s]And SORD[t]Respectively representing radar radiation sources ORD [ s ]]And ORD [ t]Random set size of (a), MatchSet (o)ORD[s],oORD[t]) Representing radar radiation sources ORD s]And ORD [ t]And (3) a matching set between the random sets, wherein the matching set is defined as a set consisting of measured value matching pairs which are respectively from the two random sets and have measured value difference not exceeding a measured value similarity threshold, and the matching set comprises the following steps:
MatchSet(oi,oj)＝{<Sip,Sjq>|Sip∈Si,Sjq∈Sj,|Sip-Sjq|≤} (2)
(9) updating the target to be compared;
and updating the index t +1 of the radar radiation source to be compared, and returning to the step 5.
(10) Initializing a cluster;
n initial clusters are constructed according to n random sets of radar radiation sources, and the random set of each cluster is respectively initialized to be the random set corresponding to the radar radiation sources, namely C1＝S1，C2＝S2，......，Cn＝Sn(ii) a Each cluster CkIs initialized to 1 and denoted as Wk1＝Wk2＝……＝Wk|Ck|1, where | Ck | represents cluster CkThe random set size of (a); the support degree of each cluster is initialized to 1, Sup1＝Sup2＝…＝Supn1 is ═ 1; membership set Memset of each clusterkInitialisation to a set of corresponding radar radiation source targets, i.e. Memsetk＝{ok}。
(11) Judging clustering;
two clusters C with minimum similarity distance in the distance matrix DistAlrray are judgeduAnd Cv(u<v) whether dist (C) is satisfiedu,Cv) ≦ continue step 12 if satisfied, and jump to step 14 if not satisfied.
(12) Clustering and fusing;
merged cluster CuAnd CvAs a new cluster CwCluster of CwThe random set elements and their weights are updated according to equations (3) and (4), respectively:
Figure BDA0001589308450000041
Figure BDA0001589308450000042
updating the support degree of the new cluster to Supw＝Supu+Supv(ii) a Member set Memset of new clusterkUpdated to Memsetw＝Memsetu+Memsetv。
(13) Updating the distance matrix;
and updating the distance matrix DistAlrray based on the new cluster, defining the distance between the new cluster and other clusters as the minimum value of the distances between the original two clusters forming the new cluster and other clusters, and returning to the step 11.
(14) Outputting in a high-frequency mode;
and finding out clusters with all support degrees meeting the threshold minsup, and outputting a corresponding random set as a high-frequency mode of the radar parameter.
The method can enhance the analysis capability of the random set type signal characteristics and can better complete the radar radiation source identification task. The method for mining the high-frequency mode of the radiation source signal parameters of the random aggregation type airborne radar based on the weighted clustering is described by an example.
Assuming that 3 samples of a type of radar radiation source are detected, the PRI signal for each sample is a random set of signal parameters, whose values are shown in table 1, in MHz.
TABLE 1
Sample(s) PRI Signal parameters (MHz)
o1 {60,69,85,100}
o2 {61,70,84,99}
o3 {70,86,101}
The similarity threshold value of the random measurement value is 0.1, the distance threshold value of the random set is 0.6, the support degree meets the threshold minsup value of 2, the minimum weight threshold minw of the measurement value is 0.3, and the method comprises the following steps:
step 1, radar radiation source targets are sorted from large to small according to random set size, and ORD (ORD-o)1＜o2＜o3；
Step 2, initializing a similarity distance matrix DistArray, the size of which is n × n, as shown in table 2:
TABLE 2
Sample(s) o1 o2 o3
o1 - ∞ ∞
o2 - - ∞
o3 - - -
Step 3, setting a current target, and setting a current radar radiation source target index s to be 1;
step 4, setting a target to be compared, and setting an index t of a radar radiation source target to be compared to be s +1 to 2;
step 5, judging that the target index value t to be compared satisfies t is less than or equal to n and size (ORD [ t ])/size (ORD [ s ]) is greater than or equal to n, and continuing to step 8;
step 8, calculating the similarity distance and calculating the radar radiation source o1And o2The similarity distance between and updating the similarity matrix DistArray, as shown in table 3:
TABLE 3
Sample(s) o1 o2 o3
o1 - 0 ∞
o2 - - ∞
o3 - - -
Step 9, updating the target to be compared, updating the radar radiation source index t to be compared to t +1 to 3, and returning to the step 5;
step 5, judging the target to be compared, judging whether the index value t of the target to be compared meets t less than or equal to n and size (ORD [ t ])/size (ORD [ s ]) is greater than or equal to n, and continuing to step 8;
step 8, calculating the similarity distance and calculating the radar radiation source o1And o3The similarity distance between and updating the similarity matrix DistArray, as shown in table 4:
TABLE 4
Sample(s) o1 o2 o3
o1 - 0 0.25
o2 - - ∞
o3 - - -
Step 9, updating the target to be compared, updating the radar radiation source index t to be compared to t +1 to 4, and returning to the step 5;
step 5, judging the target to be compared, judging whether the index value t-4 of the target to be compared does not satisfy t ≤ n and size (ORD [ t ])/size (ORD [ s ]) is greater than or equal to n, and continuing to step 6;
step 6, modifying the current target, and updating the index value s + 1-2 of the current target;
step 7, judging s < n by the current target, and returning to the step 4;
step 4, setting a target to be compared, and setting an index t of a radar radiation source target to be compared to be s +1 to 3;
step 5, judging the target to be compared, judging whether the index value t-3 of the target to be compared meets t ≤ n and size (ORD [ t ])/size (ORD [ s ]) is greater than or equal to n, and continuing to step 8;
step 8, calculating the similarity distance and calculating the radar radiation source o1And o3The similarity distance between and updating the similarity matrix DistArray, as shown in table 5:
TABLE 5
Sample(s) C1 C2 C3
C1 - 0 0.25
C2 - - 0.25
C3 - - -
Step 9, updating the target to be compared, updating the radar radiation source index t to be compared to t +1 to 4, and returning to the step 5;
step 5, judging the target to be compared, judging that the index value t of the target to be compared is 4, and the index value t does not meet the condition that t is less than or equal to n and the size (ORD [ t ])/size (ORD [ s ]) is more than or equal to n, and continuing to the step 6;
step 6, modifying the current target, and updating the index value s + 1-3 of the current target;
step 7, judging the current target, and jumping to step 10 when the index value s of the current target is larger than or equal to n;
step 10, initializing clusters, namely constructing 3 initial clusters according to n random sets of radar radiation sources, wherein the random set of each cluster is respectively initialized into a random set corresponding to the radar radiation sources, and the weight of a measured value is marked in brackets;
TABLE 6
Figure BDA0001589308450000061
Figure BDA0001589308450000071
Step 11, clustering judgment is carried out, and the distance of the similarity in the distance matrix DistAlrray is judged to be the maximumSmall two clusters C1And C2Satisfies dist (C)1,C2) Continuing the step 12 when the temperature is less than or equal to the preset temperature;
step 12, clustering fusion, merging clusters C1And C2As a new cluster C1', update the random set elements and their weights, with the measurement weights identified in parenthesis, as shown in Table 7:
TABLE 7
Sample(s) PRI Signal parameters (MHz) Sup MemSet
C1’ {60.5(1),69.5(1),85(1),100(1)} 2 {o1,o2}
C3 {70(1),86(1),101(1)} 1 {o3}
Step 13, updating the distance matrix DistAlrray based on the new cluster, and returning to the step 11;
TABLE 8
Clustering C1 C3
C1 - 0.25
C3 - -
Step 11, clustering judgment, namely judging two clusters C with the minimum similarity distance in the distance matrix DistAlrray1' and C3Satisfies dist (C)1',C3) Continuing the step 12 when the temperature is less than or equal to the preset temperature;
step 12, clustering fusion, merging clusters C1' and C3As a new cluster C1", update the random set elements and their weights, with the measurement weights identified in parentheses, as shown in table 9:
TABLE 9
Sample(s) PRI Signal parameters (MHz) Sup MemSet
C1” {60.5(0.67),69.67(1),85.3(1),100.3(1)} 3 {o1,o2,o3}
Step 13, updating distance matrix, and new clustering C1"the corresponding distance matrix is shown in table 10, and the process returns to step 11;
watch 10
Sample(s) C1”
C1” -
Step 11, judging clustering, jumping to step 14 if no corresponding clustering exists;
step 14, outputting a high-frequency mode, and finding all the clusters C with the support degrees meeting the threshold minsup-21", which corresponds to the random set {60.5,69.67,85.3,100.3} is the high frequency mode, output.
The research result of the invention is beneficial to improving the analysis capability of the signal characteristics of the random set type radar radiation source and further improving the type identification capability of the radar radiation source.
The research work of the present invention was funded by the national science foundation (No. 61771177).

Claims (1)

1. A random set type radar radiation source signal parameter high-frequency mode mining method based on weighted clustering is characterized in that: the method comprises the following steps:
(1) aiming at signal parameter data of a random set type airborne radar radiation source target, a measured value of the airborne radar radiation source signal parameter is a random set formed by a plurality of random measured values, and a similarity distance matrix is constructed;
(1.1) radar radiation source targets are grouped into random set size SORDSorting from big to small;
(1.2) initializing a similarity distance matrix with the size of n multiplied by n;
(1.3) setting a current radar radiation source target index s;
(1.4) setting a target index t of a radar radiation source to be compared;
(1.5) judging the target index to be compared, if t is not more than n and S (ORD [ t ])/S (ORD [ S ]) is more than or equal to n, continuing the step (1.6), and if t is more than or equal to n, continuing the step (1.8); the random set distance threshold is used for comparing the similarity between the random measurement value sets;
(1.6) updating the current target index value;
(1.7) judging the current target index, if s is more than or equal to n, continuing the step (3.1), and if not, returning to the step (1.4);
(1.8) calculating a similarity distance dist between radar radiation sources and updating a similarity matrix;
(1.9) updating the radar radiation source index to be compared, and returning to the step (1.5);
(2) comparing the similarity between two random set type measured values;
(3) iteratively fusing random set type clusters on the basis of a hierarchical clustering method on the basis of a similarity distance matrix until a cluster meeting a random set distance threshold cannot be found;
(3.1) constructing n initial clusters;
(3.2) judging two clusters C with minimum similarity distance in the distance matrixuAnd Cv(u<v) whether dist (C) is satisfiedu,Cv) ≦ if step (3.3) is satisfied, if not, jump to step (3.4);
(3.3) Merge clustering CuAnd CvAs a new cluster CwAnd updating;
(3.4) updating the distance matrix based on the new cluster, defining the distance between the new cluster and other clusters as the minimum value of the distances between the original two clusters forming the new cluster and other clusters, and returning to the step (3.2);
(4) meanwhile, the support degree and the random set value of each new cluster are calculated in an iterative mode, and finally, the random set value corresponding to the cluster meeting the threshold of the support degree is mined out and is used as the high-frequency mode output; and finding out clusters with all support degrees meeting a threshold minsup, judging whether the signal parameter mode belongs to high frequency, and outputting a corresponding random set as a high frequency mode of the radar parameter.
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