CN110880012B - Inter-pulse agile radar radiation source frequency information correlation method for multi-reconnaissance platform - Google Patents

Inter-pulse agile radar radiation source frequency information correlation method for multi-reconnaissance platform Download PDF

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CN110880012B
CN110880012B CN201910930123.8A CN201910930123A CN110880012B CN 110880012 B CN110880012 B CN 110880012B CN 201910930123 A CN201910930123 A CN 201910930123A CN 110880012 B CN110880012 B CN 110880012B
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reconnaissance
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CN110880012A (en
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王聪
孙宽宏
林彬
宋新超
任志明
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Yangzhou Institute Of Marine Electronic Instruments No723 Institute Of China Shipbuilding Industry Corp
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a method for correlating frequency information of a radar radiation source between pulses of multiple reconnaissance platforms, which comprises the steps of firstly classifying radar radiation source frequency samples reported by two reconnaissance platforms by using a nearest neighbor clustering algorithm, and calculating the number of frequency samples corresponding to each reconnaissance platform in each class; then respectively constructing frequency sample interception frequency vectors of two reconnaissance platforms; then, calculating cosine similarity between the two frequency sample interception frequency vectors; and finally, comparing the cosine similarity with a judgment threshold, and judging whether the frequency information of the radiation sources reported by the two reconnaissance platforms is matched. The method effectively improves the accuracy of frequency information association of the inter-pulse agile radar radiation source of the multi-reconnaissance platform.

Description

Inter-pulse agile radar radiation source frequency information correlation method for multi-reconnaissance platform
Technical Field
The invention belongs to a multi-reconnaissance platform radiation source information fusion technology, and particularly relates to a multi-reconnaissance platform inter-pulse agile radar radiation source frequency information association method.
Background
The multi-reconnaissance platform radiation source information fusion refers to comparing the radiation source information reported by each reconnaissance platform, and judging whether the information reported by each reconnaissance platform is derived from the same radar by calculating the similarity among the information such as the frequency of the radiation source, the pulse repetition interval, the pulse width and the like. In the process of multi-reconnaissance platform radiation source information fusion, the accuracy of parameter similarity calculation is very important. With the rapid development of electronic countermeasure technology, more and more military radars adopt a frequency inter-pulse agility technology. Frequency inter-pulse agility refers to the fact that the center frequency of each transmitted pulse of the radar changes rapidly at random (or in a programmed manner) within a frequency band, the frequency of the next pulse generally not being predictable from the current pulse frequency. Because the reconnaissance platform is influenced by various factors such as sensitivity of a reconnaissance receiver, relative positions between a radiation source and the reconnaissance platform, battlefield electromagnetic environment and the like in the actual detection process, certain differences exist in the information of the radiation source reported by each reconnaissance platform. For the frequency inter-pulse agile radar radiation source, the difference is not only from the deviation existing between the parameter values, but also from the number of frequency values reported by each reconnaissance platform and the difference between the interception times of each frequency value.
At present, the existing method for calculating the frequency similarity of the inter-pulse agile radar is to calculate the similarity between frequency typical values in radiation source descriptors (Emitter Description Word, EDW) reported by each reconnaissance platform, and then take the average value of the similarity as a final frequency similarity value. Because the frequency typical values with different interception times have the same weight in the calculation process, the calculated frequency similarity of the inter-pulse agile radar radiation source is usually not very accurate, and a false fusion phenomenon is easy to cause.
Disclosure of Invention
The invention aims to provide a frequency information correlation method for a pulse-to-pulse radar radiation source of a multi-reconnaissance platform.
The technical solution for realizing the purpose of the invention is as follows: a method for correlating frequency information of a pulse-to-pulse radar radiation source of a multi-reconnaissance platform comprises the following specific steps:
step 1, classifying radar radiation source frequency samples reported by two reconnaissance platforms by utilizing a nearest neighbor clustering algorithm, and calculating the number of frequency samples corresponding to the two reconnaissance platforms in each class;
step 2, respectively constructing frequency sample interception frequency vectors of two reconnaissance platforms;
step 3, calculating cosine similarity between frequency sample interception frequency vectors corresponding to the two reconnaissance platforms;
and step 4, comparing the cosine similarity with a judgment threshold, and judging whether the frequency information of the radiation sources reported by the two reconnaissance platforms is matched.
Preferably, frequency sample interception times vectors of the two reconnaissance platforms are respectively constructed as follows:
frequency sample interception times of reconnaissance platform AVector x= (X) 1 ，x 2 ，…x q ，…x CNum )；
Frequency sample interception frequency vector y= (Y) of reconnaissance platform B 1 ，y 2 ，…y q ，…y CNum ) Wherein q is more than or equal to 1 and less than or equal to CNum, x q And y q The method comprises the following steps of:
wherein CNum is the number of categories, classA_RFcnt (q) is the number of frequency samples of the q-th category corresponding to the reconnaissance platform A; classB_RFcnt (q) is the number of frequency samples of the corresponding reconnaissance platform B of the q-th class; the plata_rfnum is the total number of frequency samples contained in the radiation source information reported by the reconnaissance platform a; platB_RFNum is the number of frequency samples contained in the radiation source information reported by the reconnaissance platform B.
Preferably, the calculation formula of cosine similarity between frequency sample interception frequency vectors corresponding to the two reconnaissance platforms is as follows:
wherein ConRF is cosine similarity, and I X I is Euclidean norm of vector X, defined asThe term Y is the Euclidean norm of the vector Y, defined asX·Y＝x 1 y 1 +x 2 y 2 +…x q y q …+x CNum y CNum 。
Preferably, the specific method for judging whether the frequency information of the radiation sources reported by the two reconnaissance platforms is matched is as follows:
if ConRF is more than or equal to THR_ConRF, the frequency information of the radiation sources reported by the reconnaissance platforms A and B is matched;
if ConRF is smaller than THR_ConRF, the frequency information of the radiation sources reported by the reconnaissance platforms A and B is not matched, conRF is cosine similarity, and THR_ConRF is a decision threshold
Compared with the prior art, the invention has the remarkable advantages that: the method effectively improves the accuracy of frequency information association of the inter-pulse agile radar radiation source of the multi-reconnaissance platform.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a flow chart of correlation of frequency information of a radar radiation source of a pulse-to-pulse radar of a multi-reconnaissance platform.
Fig. 2 is a statistical histogram and timing simulation diagram of a frequency sample of the scout platform a.
FIG. 3 is a statistical histogram and timing simulation diagram of the B frequency samples of the scout platform.
Detailed Description
A method for correlating frequency information of a pulse-to-pulse radar radiation source of a multi-reconnaissance platform comprises the following specific steps:
step 1, classifying radar radiation source frequency samples reported by two reconnaissance platforms by using a nearest neighbor clustering algorithm, and calculating the number of frequency samples corresponding to the two reconnaissance platforms in each class, wherein the method comprises the following specific steps of:
step 1-1, two reconnaissance platforms A and B respectively detect a radar radiation source signal of a certain inter-pulse agility, the reconnaissance platform A reports the radiation source information to the fusion center as PlatA_EDW, and the reconnaissance platform B reports the radiation source information to the fusion center as PlatB_EDW.
Extracting a frequency sample sequence { PlatA_RF from radiation source information PlatA_EDW reported by a reconnaissance platform A i And (1.ltoreq.i.ltoreq.PlatA_RFNum), wherein PlatA_RFNum is the number of frequency samples reported by the reconnaissance platform A.
Extracting a frequency sample sequence { PlatB_RF from radiation source information PlatB_EDW reported by a reconnaissance platform B j And (1.ltoreq.j.ltoreq.PlatB_RFNum), wherein PlatB_RFNum is the number of frequency samples reported by the reconnaissance platform B.
Combining the frequency sample sequences reported by the reconnaissance platform A and the reconnaissance platform B, and marking the combined frequency sample sequences as { RF } k And (1.ltoreq.k.ltoreq.RFNum), wherein RFNum=PlatA_RFNum+PlatB_RFNum.
Step 1-2, determining a clustered frequency decision threshold thr_rf according to the frequency measurement accuracy of the two reconnaissance platforms, namely:
wherein sigma A For detecting the frequency measurement precision of the platform A, sigma B The frequency measurement precision of the reconnaissance platform B is obtained.
Step 1-3, let k=1, cnum=0, where k is the sequence of frequency samples { RF } k Subscript of } (1.ltoreq.k.ltoreq.RFNum), CNum is the number of categories currently established.
Step 1-4, reading the sequence of frequency samples { RF k K-th frequency sample in }.
Step 1-5, judging whether the number of the currently established categories CNum is zero, if CNum=0, indicating that the categories are not established, and executing step 1-12 by firstly establishing a new category; if CNum > 0, then it is stated that a class has been established, class matching is required, and steps 1-6 are performed.
Step 1-6, calculating a frequency sample RF k Center frequency class_rf with currently established CNum categories c The difference DeltaRF between (1.ltoreq.c.ltoreq.CNum) c (1. Ltoreq.c. Ltoreq.CNum), namely:
ΔRF c ＝|RF k -Class_RF c i, wherein c is more than or equal to 1 and less than or equal to CNum
Step 1-7, find the difference DeltaRF c Minimum value DeltaRF in (1.ltoreq.c.ltoreq.CNum) min And its corresponding class number CIndex.
Step 1-8, deltaRF min Comparing with the decision threshold THR_RF, if DeltaRF min And +.THR-RF, then the frequency sample RF is described k Matching with the CIndex category, and executing the steps 1-9; if DeltaRF min > THR_RF, then the frequency samples RF are described k And (3) performing steps 1-12 without matching with the established category.
Step 1-9, calculating the center frequency class_RF of the CIndex th Class CIndex And the number of frequency samples class_num CIndex The method comprises the following steps:
Class_Num CIndex ＝Class_Num CIndex +1
step 1-10 based on frequency sample RF k The source adds 1 to the frequency sample interception frequency of the reconnaissance platform corresponding to the CIndex category, namely:
if RF k From reconnaissance platform a, then let classa_rfcnt (CIndex) =classa_rfcnt (CIndex) +1;
if RF k From reconnaissance platform B, then let classb_rfcnt (CIndex) =classb_rfcnt (CIndex) +1;
step 1-11, directly executing step 1-14.
Steps 1-12, based on the kth frequency sample RF k A new category is established, namely: let cnum=cnum+1, the new Class center frequency is class_rf CNum ＝RF k The number of frequency samples within the Class class_num CNum ＝1。
Step 1-13 based on frequency sample RF k Initializing the frequency sample interception frequency classa_rfcnt (CNum) of the reconnaissance platform a corresponding to the new category CNum, namely, the frequency sample interception frequency classb_rfcnt (CNum) corresponding to the reconnaissance platform B:
if RF k Originating from the reconnaissance platform a, then let classa_rfcnt (CNum) =1, classb_rfcnt (CNum) =0;
if RF k Originating from the reconnaissance platform B, then let classa_rfcnt (CNum) =0, classb_rfcnt (CNum) =1.
Step 1-14, reading the next frequency sample, let k=k+1Judging whether k > RFNum is true, if true, then describing the frequency sample sequence { RF k Performing step 2 after classification of (1.ltoreq.k.ltoreq.RFNum); if not, then the sequence of frequency samples { RF k And (c) the classification of (1.ltoreq.k.ltoreq.RFNum) is not completed, and the step b) is returned.
Step 2, constructing a frequency sample interception frequency vector x= (X) of the reconnaissance platform a 1 ，x 2 ，…x q ，…x CNum ) And frequency sample interception frequency vector y= (Y) of reconnaissance platform B 1 ，y 2 ，…y q ，…y CNum ) Wherein q is more than or equal to 1 and less than or equal to CNum, x q And y q The method comprises the following steps of:
in the above formula, CNum is the number of categories obtained after the clustering in the step 1 is completed, and ClassA_RFcnt (q) is the number of frequency samples of the reconnaissance platform A corresponding to the q-th category; classB_RFcnt (q) is the number of frequency samples of the reconnaissance platform B corresponding to the q-th category; the plata_rfnum is the total number of frequency samples contained in the radiation source information reported by the reconnaissance platform a; platB_RFNum is the number of frequency samples contained in the radiation source information reported by the reconnaissance platform B.
Step 3, calculating cosine similarity between the frequency sample interception frequency vector X of the reconnaissance platform A and the frequency sample interception frequency vector Y of the reconnaissance platform B, namely:
where X is the Euclidean norm of vector X, defined asThe term Y is used to denote the Europe of the vector YThe several norms, defined as +.>X·Y＝x 1 y 1 +x 2 y 2 +…x q y q …+x CNum y CNum 。
Step 4, comparing the obtained cosine similarity ConRF with a judgment threshold THR_ConRF, and if the ConRF is more than or equal to THR_ConRF, indicating that the radiation source frequency information reported by the reconnaissance platforms A and B is matched; if ConRF < THR_ConRF, then it is indicated that the source frequency information reported by the scout platforms A and B does not match.
Examples
1 part of inter-pulse agile radar signals are simulated by a radar signal simulator, and detailed parameter settings are shown in table 1.
TABLE 1
Fig. 2 and 3 show a statistical histogram and a timing chart of frequency samples reported by the reconnaissance platform a and the reconnaissance platform B, respectively. The frequency measurement precision of the reconnaissance platform A is higher, the pulse loss probability is smaller (< 5%), the frequency measurement precision of the reconnaissance platform B is lower, and the pulse loss probability is larger (> 15%). And merging the frequency sample sequences reported by the reconnaissance platform A and the reconnaissance platform B, classifying the merged frequency sample sequences by using nearest neighbor-based clustering calculation, and obtaining the category obtained after clustering and the number of the frequency samples corresponding to the two reconnaissance platforms in each category, wherein the number of the frequency samples is shown in a table 2.
TABLE 2
Constructing a frequency sample interception frequency vector X of the reconnaissance platform A and a frequency sample interception frequency vector Y of the reconnaissance platform B, obtaining a similarity of 0.9003 between the frequency samples of the reconnaissance platform A and the frequency samples of the reconnaissance platform B by utilizing a cosine similarity calculation formula, and judging that the frequency samples reported by the reconnaissance platform A and the reconnaissance platform B are matched when the judgment threshold value is 0.7. The frequency similarity obtained by the conventional method is 0.8125, and the frequency similarity calculated by the conventional method has low accuracy because the frequency with fewer occurrences typically has the same weight in the process of calculating the frequency similarity.

Claims (3)

1. A method for correlating frequency information of a pulse-to-pulse radar radiation source of a multi-reconnaissance platform is characterized by comprising the following specific steps:
step 1, classifying radar radiation source frequency samples reported by two reconnaissance platforms by using a nearest neighbor clustering algorithm, and calculating the number of frequency samples corresponding to the two reconnaissance platforms in each class, wherein the specific method comprises the following steps:
step 1-1, extracting frequency sample sequences from radiation sources reported by two platforms respectively and combining the frequency sample sequences;
step 1-2, determining a clustered frequency decision threshold thr_rf according to the frequency measurement accuracy of the two reconnaissance platforms, namely:
wherein sigma A For detecting the frequency measurement precision of the platform A, sigma B The frequency measurement precision of the reconnaissance platform B is obtained;
step 1-3, let k=1, cnum=0, where k is the subscript of the combined frequency sample sequence, and CNum is the number of currently established categories;
step 1-4, reading the sequence of frequency samples { RF k K-th frequency sample in };
step 1-5, judging whether the number of categories CNum currently established is zero, and if CNum=0, executing step 1-12; if CNum > 0, executing step 1-6;
step 1-6, calculating a frequency sample RF k Center frequency class_rf with currently established CNum categories c Difference DeltaRF between c ；
Step 1-7, find the difference DeltaRF c Is a minimum value DeltaRF of (a) min And its corresponding class number CIndex;
step 1-8, the difference DeltaRF min Comparing with the decision threshold THR_RF, if DeltaRF min THR-RF, then the frequency sample RF k Matching with the CIndex category, and executing the steps 1-9; if DeltaRF min > THR_RF, then the frequency sample RF k Step 1-12 is executed if the category is not matched with the established category;
step 1-9, calculating the center frequency class_RF of the CIndex th Class CIndex And the number of frequency samples class_num CIndex The method comprises the following steps:
Class_Num CIndex ＝Class_Num CIndex +1
step 1-10 based on frequency sample RF k The source is that the frequency sample interception times of the reconnaissance platform corresponding to the CIndex class is added with 1;
step 1-11, directly executing step 1-14;
steps 1-12, based on the kth frequency sample RF k A new category is established, namely: let cnum=cnum+1, the new Class center frequency is class_rf CNum ＝RF k The number of frequency samples within the Class class_num CNum ＝1；
Step 1-13 based on frequency sample RF k Initializing the frequency sample interception frequency classa_rfcnt (CNum) of the reconnaissance platform a corresponding to the new category CNum, namely, the frequency sample interception frequency classb_rfcnt (CNum) corresponding to the reconnaissance platform B:
if RF k Originating from the reconnaissance platform a, then let classa_rfcnt (CNum) =1,
ClassB_RFcnt(CNum)＝0；
if RF k From the reconnaissance platform B, then let classa_rfcnt (CNum) =0,
ClassB_RFcnt(CNum)＝1；
step 1-14, reading the next frequency sample, let k=k+1, judging whether k > RFNum is true, if true, the frequency sample sequence { RF k The classification is completed, k is more than or equal to 1 and less than or equal to RFNum, RFNum=PlatA_RFNum+PlatB_RFNum, and PlatA_RFNum is the total number of frequency samples contained in the radiation source information reported by the reconnaissance platform A; the PlatB_RFNum is the total number of frequency samples contained in the radiation source information reported by the reconnaissance platform B; if not, then the sequence of frequency samples { RF k Incomplete classification, returning to the step 1-4, wherein k is more than or equal to 1 and less than or equal to RFNum;
step 2, respectively constructing frequency sample interception frequency vectors of two reconnaissance platforms, wherein the frequency sample interception frequency vectors are respectively specifically as follows:
frequency sample interception frequency vector x= (X) of reconnaissance platform a 1 ，x 2 ，…x q ，…x CNum )；
Frequency sample interception frequency vector y= (Y) of reconnaissance platform B 1 ，y 2 ，…y q ，…y CNum ) Wherein q is more than or equal to 1 and less than or equal to CNum, x q And y q The method comprises the following steps of:
wherein CNum is the number of categories, classA_RFcnt (q) is the number of frequency samples of the q-th category corresponding to the reconnaissance platform A; classB_RFcnt (q) is the number of frequency samples of the corresponding reconnaissance platform B of the q-th class;
step 3, calculating cosine similarity between frequency sample interception frequency vectors corresponding to the two reconnaissance platforms;
and step 4, comparing the cosine similarity with a judgment threshold, and judging whether the frequency information of the radiation sources reported by the two reconnaissance platforms is matched.
2. The method for correlating frequency information of a multiple reconnaissance platform inter-pulse radar radiation source according to claim 1, wherein a calculation formula of cosine similarity between frequency sample interception frequency vectors corresponding to two reconnaissance platforms is:
wherein ConRF is cosine similarity, and I X I is Euclidean norm of vector X, defined asThe term Y is the Euclidean norm of the vector Y, defined asX·Y＝x 1 y 1 +x 2 y 2 +…x q y q …+x CNum y CNum 。
3. The method for correlating the frequency information of the inter-pulse agile radar radiation sources of the multiple reconnaissance platforms according to claim 1, wherein the specific method for judging whether the frequency information of the radiation sources reported by the two reconnaissance platforms is matched is as follows:
if ConRF is more than or equal to THR_ConRF, the frequency information of the radiation sources reported by the reconnaissance platforms A and B is matched;
if ConRF is less than THR_ConRF, the frequency information of the radiation sources reported by the reconnaissance platforms A and B is not matched, conRF is cosine similarity, and THR_ConRF is a decision threshold.
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