CN110880012A - Correlation method for frequency information of agile radar radiation source between multiple scout platforms - Google Patents

Correlation method for frequency information of agile radar radiation source between multiple scout platforms Download PDF

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CN110880012A
CN110880012A CN201910930123.8A CN201910930123A CN110880012A CN 110880012 A CN110880012 A CN 110880012A CN 201910930123 A CN201910930123 A CN 201910930123A CN 110880012 A CN110880012 A CN 110880012A
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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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Abstract

The invention discloses a frequency information correlation method for an inter-pulse agility radar radiation source of multiple reconnaissance platforms, which comprises the steps of 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 each reconnaissance platform in each category; then, frequency sample interception frequency vectors of the two reconnaissance platforms are constructed respectively; 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 radiation source frequency information reported by the two reconnaissance platforms is matched. The method effectively improves the accuracy of the frequency information association of the agile radar radiation source among the pulses of the multiple reconnaissance platforms.

Description

Correlation method for frequency information of agile radar radiation source between multiple scout platforms
Technical Field
The invention belongs to a multi-reconnaissance platform radiation source information fusion technology, and particularly relates to a frequency information correlation method for an agile radar radiation source between pulses of multiple reconnaissance platforms.
Background
The multi-reconnaissance platform radiation source information fusion refers to the steps of comparing radiation source information reported by each reconnaissance platform, and judging whether the information reported by each reconnaissance platform is from the same radar or not by calculating the similarity among the radiation source frequency, the pulse repetition interval, the pulse width and other information. In the multi-reconnaissance platform radiation source information fusion process, the accuracy of parameter similarity calculation is very important. With the rapid development of electronic countermeasure technology, more and more military radars adopt frequency pulse agility technology. Frequency-to-pulse agility refers to the fact that the center frequency of each transmitted pulse of a radar changes randomly (or in a programmed manner) and rapidly within a certain frequency band, and the frequency of the next pulse cannot be predicted by the current pulse frequency generally. Because the reconnaissance platform is influenced by various factors such as the sensitivity of the reconnaissance receiver, the relative position between the radiation source and the reconnaissance platform, the electromagnetic environment of the battlefield and the like in the actual reconnaissance process, the radiation source information reported by each reconnaissance platform generally has certain difference. For the frequency and pulse agility radar radiation source, the difference not only comes from the deviation among the parameter values, but also comes from the difference among the number of frequency values reported by each reconnaissance platform and the interception times of each frequency value.
At present, the existing method for calculating frequency similarity of an inter-pulse agility radar is to calculate similarity between frequency typical values in an Emitter Description Word (EDW) reported by each scout platform, and then take an 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 rapid radar radiation source between pulses is usually not very accurate, and the phenomenon of false fusion is easily caused.
Disclosure of Invention
The invention aims to provide a frequency information correlation method for an agile radar radiation source between multiple scout platforms.
The technical solution for realizing the purpose of the invention is as follows: a method for correlating frequency information of agile radar radiation sources among multiple scout platforms pulses 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 category;
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 radiation source frequency information reported by the two reconnaissance platforms is matched.
Preferably, the frequency sample interception times vectors for constructing the two reconnaissance platforms are respectively as follows:
the frequency sample interception times vector X of the reconnaissance platform A is (X)1，x2，…xq，…xCNum)；
The frequency sample interception times vector Y of the reconnaissance platform B is (Y)1，y2，…yq，…yCNum) Wherein q is more than or equal to 1 and less than or equal to CNum, xqAnd yqThe method comprises the following steps:
Figure BDA0002220015830000021
Figure BDA0002220015830000022
in the formula, CNum is the number of categories, and ClassA _ rfcnt (q) is the number of frequency samples of the detection platform a corresponding to the qth category; ClassB _ rfcnt (q) is the number of frequency samples corresponding to the scout platform B for the qth class; 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 the cosine similarity between the frequency sample interception frequency vectors corresponding to the two reconnaissance platforms is as follows:
Figure BDA0002220015830000023
wherein ConRF is cosine similarity and | X | isIs the Euclidean norm of vector X, defined as
Figure BDA0002220015830000024
Y is the Euclidean norm of vector Y, defined as
Figure BDA0002220015830000025
X·Y＝x1y1+x2y2+…xqyq…+xCNumyCNum。
Preferably, the specific method for judging whether the frequency information of the radiation source reported by the two reconnaissance platforms is matched is as follows:
if the ConRF is larger than or equal to the THR _ ConRF, the radiation source frequency information reported by the reconnaissance platforms A and B are matched;
if the ConRF is less than the THR _ ConRF, the radiation source frequency information reported by the reconnaissance platforms A and B are not matched, the ConRF is cosine similarity, and the THR _ ConRF is a decision threshold
Compared with the prior art, the invention has the following remarkable advantages: the method effectively improves the accuracy of the frequency information association of the agile radar radiation source among the pulses of the multiple reconnaissance platforms.
The present invention is described in further detail below with reference to the attached drawings.
Drawings
Fig. 1 is a flow chart of correlation of frequency information of an agile radar radiation source among multiple scout platforms.
Fig. 2 is a statistical histogram and timing simulation of scout platform a frequency samples.
Fig. 3 is a statistical histogram and timing simulation of scout platform B frequency samples.
Detailed Description
A method for correlating frequency information of agile radar radiation sources among multiple scout platforms pulses comprises the following specific steps:
the method comprises the following steps of 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 category, wherein the method specifically comprises the following steps:
step 1-1, two reconnaissance platforms A and B respectively reconceive a radiation source signal of an inter-pulse agility radar, the reconnaissance platform A reports radiation source information to a fusion center and records the radiation source information as PlatA _ EDW, and the reconnaissance platform B reports the radiation source information to the fusion center and records the radiation source information as PlatB _ EDW.
Frequency sample sequence { PlatA _ RF) is extracted from radiation source information PlatA _ EDW reported by the reconnaissance platform AiAnd (i is more than or equal to 1 and less than or equal to PlatA _ RFNum), wherein the PlatA _ RFNum is the number of frequency samples reported by the reconnaissance platform A.
Extracting a frequency sample sequence { PlatB _ RF from the radiation source information PlatB _ EDW reported by the reconnaissance platform BjAnd j is more than or equal to 1 and less than or equal to PlatB _ RFNum, wherein the PlatB _ RFNum is the number of frequency samples reported by the reconnaissance platform B.
Merging the frequency sample sequences reported by the reconnaissance platform A and the reconnaissance platform B, and recording the merged frequency sample sequences as { RF }kAnd (k is more than or equal to 1 and less than or equal to RFNum), wherein the RFNum is PlatA _ RFNum + PlatB _ RFNum.
Step 1-2, determining a clustered frequency decision threshold THR _ RF according to the frequency measurement precision of the two reconnaissance platforms, namely:
Figure BDA0002220015830000031
wherein sigmaAFor investigating the frequency measurement accuracy of the platform A, sigmaBTo reconnaissance the frequency measurement accuracy of the platform B.
Step 1-3, let k be 1, CNum be 0, where k is a sequence of frequency samples { RF }kThe subscript of (1 ≦ k ≦ RFNum), and CNum is the number of currently established categories.
Step 1-4, read frequency sample sequence { RFkThe kth frequency sample in (j).
Step 1-5, judging whether the number CNum of the currently established categories is zero, if CNum is 0, indicating that no category is established, establishing a new category first, and executing step 1-12; if CNum > 0, then indicate that a class has been established, a class match is required, and steps 1-6 are performed.
Step 1-6, calculating frequency sample RFkCenter frequency Cl of CNum classes established currentlyass_RFc(1. ltoreq. c. ltoreq. CNum) difference Δ RFc(1. ltoreq. c. ltoreq. CNum), namely:
ΔRFc＝|RFk-Class_RFcl, where c is not less than 1 and not more than CNum
Step 1-7, finding out difference value delta RFcMinimum value DeltaRF in (1. ltoreq. c. ltoreq. CNum)minAnd its corresponding class number CIndex.
Step 1-8, converting Δ RFminComparing with the decision threshold THR _ RF if Δ RFminTHR _ RF ≦ then the frequency sample RF is accounted forkMatching with the second CIndex category, and executing the steps 1-9; if Δ RFminTHR _ RF, then the frequency sample RF is illustratedkWithout matching any of the established categories, steps 1-12 are performed.
Step 1-9, calculating the center frequency Class _ RF of the third ClassCIndexAnd number of frequency samples Class _ NumCIndexNamely:
Figure BDA0002220015830000041
Class_NumCIndex＝Class_NumCIndex+1
steps 1-10, sample RF according to frequencykAnd (3) the source adds 1 to the frequency sample interception frequency of the reconnaissance platform corresponding to the third CIndex type, namely:
if RFkFrom reconnaissance platform a, let ClassA _ rfcnt (cindex) ═ ClassA _ rfcnt (cindex) + 1;
if RFkOriginating from scout platform B, let ClassB _ rfcnt (cindex) ═ ClassB _ rfcnt (cindex) + 1;
steps 1-11, directly performing steps 1-14.
Steps 1-12 sample RF according to the k-th frequencykA new category is established, namely: let CNum be CNum +1, the new Class center frequency is Class _ RFCNum＝RFkNumber of frequency samples Class _ Num within this ClassCNum＝1。
Steps 1-13, sample RF according to frequencykInitializing the detection corresponding to the new category CNumThe frequency sample interception times ClassA _ rfcnt (cnum) of the reconnaissance platform a and the frequency sample interception times ClassB _ rfcnt (cnum) corresponding to the reconnaissance platform B are as follows:
if RFkOriginating from scout platform a, let ClassA _ rfcnt (cnum) be 1, ClassB _ rfcnt (cnum) be 0;
if RFkFrom the scout platform B, ClassA _ rfcnt (cnum) ═ 0 and ClassB _ rfcnt (cnum) ═ 1 are given.
Step 1-14, reading the next frequency sample, making k equal to k +1, judging whether k > RFNum is true, if true, explaining the frequency sample sequence { RF { (RF) }kFinishing the classification (k is more than or equal to 1 and less than or equal to RFNum), and executing the step 2; if not, then the sequence of frequency samples RF is interpretedkAnd f, finishing the classification (k is more than or equal to 1 and less than or equal to RFNum), and returning to the step b).
Step 2, constructing a frequency sample interception frequency vector X ═ X of the reconnaissance platform A1，x2，…xq，…xCNum) And the frequency sample interception times vector Y of the reconnaissance platform B is (Y)1，y2，…yq，…yCNum) Wherein q is more than or equal to 1 and less than or equal to CNum, xqAnd yqThe method comprises the following steps:
Figure BDA0002220015830000051
Figure BDA0002220015830000052
in the above formula, CNum is the number of categories obtained after the clustering in step 1 is completed, and ClassA _ rfcnt (q) is the number of frequency samples of the reconnaissance platform a corresponding to the qth category; ClassB _ rfcnt (q) is the number of frequency samples of the scout platform B corresponding to the qth category; 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 times vector X of the reconnaissance platform A and the frequency sample interception times vector Y of the reconnaissance platform B, namely:
Figure BDA0002220015830000061
where X is the Euclidean norm of vector X, defined as
Figure BDA0002220015830000062
Y is the Euclidean norm of vector Y, defined as
Figure BDA0002220015830000063
X·Y＝x1y1+x2y2+…xqyq…+xCNumyCNum。
Step 4, comparing the obtained cosine similarity ConRF with a decision threshold THR _ ConRF, and if the ConRF is more than or equal to the THR _ ConRF, indicating that the radiation source frequency information reported by the reconnaissance platforms A and B is matched; if ConRF < THR _ ConRF, it indicates that the radiation source frequency information reported by the reconnaissance platforms A and B do not match.
Examples
The radar signal simulator is used for simulating 1 part of pulse-to-pulse agile radar signals, and the detailed parameter settings are shown in table 1.
TABLE 1
Figure BDA0002220015830000064
Fig. 2 and fig. 3 are a statistical histogram and a timing chart of frequency samples reported by the scout platform a and the scout platform B, respectively. The frequency measurement precision of the reconnaissance platform A is high, the pulse loss probability is small (< 5%), the frequency measurement precision of the reconnaissance platform B is low, and the pulse loss probability is large (> 15%). Merging the frequency sample sequences reported by the reconnaissance platform A and the reconnaissance platform B, classifying the merged frequency sample sequences by utilizing nearest neighbor clustering, wherein the categories obtained after clustering and the frequency sample numbers corresponding to the two reconnaissance platforms in each category are shown in a table 2.
TABLE 2
Figure BDA0002220015830000065
Figure BDA0002220015830000071
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 the similarity between the frequency sample of the reconnaissance platform A and the frequency sample of the reconnaissance platform B as 0.9003 by utilizing a cosine similarity calculation formula, and judging that the frequency samples reported on the reconnaissance platform A and the reconnaissance platform B are matched when the judgment threshold value is 0.7. The frequency similarity obtained by using the conventional method is 0.8125, and since the frequencies with fewer occurrences typically have the same weight in the process of calculating the frequency similarity, the accuracy of the frequency similarity calculated by using the conventional method is low.

Claims (5)

1. A method for correlating frequency information of agile radar radiation sources among multiple reconnaissance platforms 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 category;
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 radiation source frequency information reported by the two reconnaissance platforms is matched.
2. The inter-pulse agility radar radiation source frequency information correlation method according to claim 1, wherein step 1 classifies radar radiation source frequency samples reported by two reconnaissance platforms by using a nearest neighbor clustering algorithm, and the specific method for calculating the number of frequency samples corresponding to the two reconnaissance platforms in each category is as follows:
step 1-1, extracting frequency sample sequences from radiation sources reported by two platforms respectively and merging the frequency sample sequences;
step 1-2, determining a clustered frequency decision threshold THR _ RF according to the frequency measurement precision of the two reconnaissance platforms, namely:
Figure FDA0002220015820000011
wherein σAFor investigating the frequency measurement accuracy of the platform A, sigmaBThe frequency measurement precision of the reconnaissance platform B;
step 1-3, making k equal to 1 and CNum equal to 0, wherein k is a subscript of the combined frequency sample sequence, and CNum is the number of currently established categories;
step 1-4, read frequency sample sequence { RFkThe first inkA number of frequency samples;
step 1-5, judging whether the currently established category number CNum is zero, and if CNum is 0, executing step 1-12; if CNum > 0, execute steps 1-6;
step 1-6, calculating frequency sample RFkWith the center frequencies Class _ RF of the currently established CNum classescDifference Δ RF betweenc；
Step 1-7, finding out difference value delta RFcMinimum value of Δ RFminAnd the corresponding class serial number CIndex;
step 1-8, the difference value Delta RFminComparing with the decision threshold THR _ RF if Δ RFminTHR _ RF ≦ frequency sample RFkMatching with the second CIndex category, and executing the steps 1-9; if Δ RFminGreater than THR _ RF, then frequency sample RFkIf the classification is not matched with the established classification, executing the steps 1-12;
step 1-9, calculating the center frequency Class _ RF of the third ClassCIndexAnd number of frequency samples Class _ NumCIndexNamely:
Figure FDA0002220015820000021
Class_NumCIndex＝Class_NumCIndex+1
steps 1-10, sample RF according to frequencykThe source adds 1 to the frequency sample interception frequency of the reconnaissance platform corresponding to the CIndex type;
step 1-11, directly executing step 1-14;
steps 1-12 sample RF according to the k-th frequencykA new category is established, namely: let CNum be CNum +1, the new Class center frequency is Class _ RFCNum＝RFkNumber of frequency samples Class _ Num within this ClassCNum＝1；
Steps 1-13, sample RF according to frequencykInitializing frequency sample interception times ClassA _ RFcnt (CNum) of the reconnaissance platform A corresponding to the new type CNum and frequency sample interception times ClassB _ RFcnt (CNum) corresponding to the reconnaissance platform B, namely:
if RFkFrom reconnaissance platform a, let ClassA _ rfcnt (cnum) 1,
ClassB_RFcnt(CNum)＝0；
if RFkOriginating from scout platform B, let ClassA _ rfcnt (cnum) be 0,
ClassB_RFcnt(CNum)＝1；
step 1-14, reading the next frequency sample, making k equal to k +1, judging whether k > RFNum is true, if true, then the frequency sample sequence { RFk}(1≤kLess than or equal to RFNum) classification is finished; if not, the sequence of frequency samples { RF }kAnd (k is more than or equal to 1 and less than or equal to RFNum), the classification is not finished, and the step 1-4 is returned.
3. The correlation method for frequency information of the agile radar radiation source between multiple scout platforms according to claim 1, wherein the frequency sample interception times vectors for constructing the two scout platforms are respectively:
the frequency sample interception times vector X of the reconnaissance platform A is (X)1，x2，…xq，…xCNum)；
The frequency sample interception times vector Y of the reconnaissance platform B is (Y)1，y2，…yq，…yCNum) Wherein q is more than or equal to 1 and less than or equal to CNum, xqAnd yqThe method comprises the following steps:
Figure FDA0002220015820000031
Figure FDA0002220015820000032
in the formula, CNum is the number of categories, and ClassA _ rfcnt (q) is the number of frequency samples of the detection platform a corresponding to the qth category; ClassB _ rfcnt (q) is the number of frequency samples corresponding to the scout platform B for the qth class; 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.
4. The correlation method for the frequency information of the agile radar radiation source between the pulses of the multiple scouting platforms according to claim 1, wherein the calculation formula of the cosine similarity between the frequency sample interception frequency vectors corresponding to the two scouting platforms is as follows:
Figure FDA0002220015820000033
where ConRF is the cosine similarity and | X | is the Euclidean norm of the vector X and is defined as
Figure FDA0002220015820000034
Y is the Euclidean norm of vector Y, defined as
Figure FDA0002220015820000035
X·Y＝x1y1+x2y2+…xqyq…+xCNumyCNum。
5. The correlation method for the frequency information of the agile radar radiation source between the multiple scout platforms according to claim 1, wherein the specific method for judging whether the frequency information of the radiation source reported by the two scout platforms is matched is as follows:
if the ConRF is larger than or equal to the THR _ ConRF, the radiation source frequency information reported by the reconnaissance platforms A and B are matched;
if the ConRF is less than the THR _ ConRF, the radiation source frequency information reported by the reconnaissance platforms A and B are not matched, the ConRF is cosine similarity, and the THR _ ConRF is a decision threshold.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112131447A (en) * 2020-09-29 2020-12-25 中国船舶重工集团公司第七二四研究所 Radar signal detection data sample labeling method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4896159A (en) * 1987-05-26 1990-01-23 Selenia, Industrie Elettroniche Associate, S.P.A. Radar system with pulse-to-pulse frequency agility and multiple-time-around echo resolution
CN104794431A (en) * 2015-03-25 2015-07-22 中国电子科技集团公司第二十八研究所 Radar radiation source pulse-to-pulse mode excavation method based on fuzzy matching
CN105787081A (en) * 2016-03-03 2016-07-20 电子科技大学 Radiation platform correlation method based on radiation source space positions
CN107656245A (en) * 2017-08-22 2018-02-02 哈尔滨工程大学 A kind of method being applied to information fusion in radar signal sorting
CN108919193A (en) * 2018-07-12 2018-11-30 中国船舶重工集团公司第七二四研究所 A kind of parameter agile radar signal sorting method excavated based on sequence fragment
CN109726734A (en) * 2018-11-23 2019-05-07 山东航天电子技术研究所 A kind of target platform automatic recognition system based on radiation source reconnaissance

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4896159A (en) * 1987-05-26 1990-01-23 Selenia, Industrie Elettroniche Associate, S.P.A. Radar system with pulse-to-pulse frequency agility and multiple-time-around echo resolution
CN104794431A (en) * 2015-03-25 2015-07-22 中国电子科技集团公司第二十八研究所 Radar radiation source pulse-to-pulse mode excavation method based on fuzzy matching
CN105787081A (en) * 2016-03-03 2016-07-20 电子科技大学 Radiation platform correlation method based on radiation source space positions
CN107656245A (en) * 2017-08-22 2018-02-02 哈尔滨工程大学 A kind of method being applied to information fusion in radar signal sorting
CN108919193A (en) * 2018-07-12 2018-11-30 中国船舶重工集团公司第七二四研究所 A kind of parameter agile radar signal sorting method excavated based on sequence fragment
CN109726734A (en) * 2018-11-23 2019-05-07 山东航天电子技术研究所 A kind of target platform automatic recognition system based on radiation source reconnaissance

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
秦长海;: "一种基于蚁群算法的多平台ESM信息数据融合方法" *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112131447A (en) * 2020-09-29 2020-12-25 中国船舶重工集团公司第七二四研究所 Radar signal detection data sample labeling method

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