Adaptive weight updating radiation source target identification method and system

Abstract

The invention belongs to the field of signal modulation identification, and provides an adaptive weight updating radiation source target identification method and system, and the method comprises the steps: obtaining a to-be-identified radiation source signal, and selecting a feature vector to obtain a reference sequence; forming an identification matrix by the reference sequence and the comparison sequence, and normalizing each sequence of the identification matrix to obtain a to-be-identified matrix; calculating a weighted grey correlation degree of each column in the matrix to be identified by adopting a grey correlation analysis method; the weighted grey correlation degrees obtained through calculation are ranked in size, the comparison sequence corresponding to the maximum weighted grey correlation degree is determined, and the reference sequence is similar to the comparison sequence. According to the invention, a Gaussian normalization method is provided to normalize pulse parameters, and an alpha filter is used to update the currently obtained weight, so that the estimation precision of the weight is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of signal modulation recognition, and in particular relates to a radiation source target recognition method and system with adaptive weight updating. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Radar emitter target identification is the process of comparing the newly measured emitter signal parameters with the accumulated measured parameters, and determining the emitter properties through certain mathematical processing methods. It is a key step in the process of electronic countermeasures and electronic intelligence reconnaissance. Modern radar systems are becoming more and more diversified, and new radar systems are complex and changeable, resulting in increased uncertainty in the measurement values ​​of the emitter characteristic parameters. The characteristics are often manifested in various forms such as agility and jitter. At the same time, at the test site, using the existing small amount of data to meet the requirements of real-time and accurate radar signal identification is a difficult problem faced by electronic countermeasures and electronic intelligence reconnaissance.

[0004] For radar signal recognition, template matching method has fast recognition speed. Fuzzy matching method can effectively suppress noise and has strong fault tolerance. Traditional grey correlation analysis method has the characteristics of low computational complexity and good recognition effect, and can also solve the problem of low recognition accuracy caused by parameter default and large data error.

[0005] Among the existing radar signal recognition methods: the template matching method is greatly affected by the signal characteristic parameter measurement method and environmental noise, and has limited recognition ability for new system radiation sources; the fuzzy matching method is insufficient for radar signals with variable modulation parameters. The normalization method of the traditional gray correlation method is relatively simple and has no specificity for the frequency of frequency agile and jitter radar signals. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a radiation source target identification method and system with adaptive weight updating. Based on the traditional grey correlation method, the present invention aims at the problem that the frequency dimension of frequency agile and jitter radiation source signals does not have clustering, which easily causes the target to be separated into multiple pulse trains after clustering and sorting. A Gaussian normalization method is proposed to normalize the pulse parameters, and an α filter is used to update the current weights, thereby improving the estimation accuracy of the weights.

[0007] According to some embodiments, a first solution of the present invention provides a radiation source target recognition method with adaptive weight update, which adopts the following technical solution:

[0008] A radiation source target recognition method with adaptive weight updating, comprising:

[0009] Acquire the radiation source signal to be identified, and select the characteristic vector to obtain a reference sequence;

[0010] The reference number series and the comparison number series are combined into an identification matrix, and each column of the identification matrix is ​​normalized to obtain a matrix to be identified;

[0011] The grey relational analysis method is used to calculate the weighted grey relational degree of each column in the matrix to be identified;

[0012] The calculated weighted grey correlation degrees are sorted by size, and the comparison sequence corresponding to the largest weighted grey correlation degree is determined. If the reference sequence is similar to the comparison sequence, the radiation source target is identified.

[0013] Furthermore, the reference sequence and the pulse signal of the comparison sequence are fused to obtain a recognition matrix;

[0014] After the pulse signals are fused, the characteristic vector parameters of the comparison sequence and the reference sequence are updated.

[0015] Furthermore, the updating of the characteristic vector parameters of the comparison sequence and the reference sequence is specifically as follows:

[0016]

[0017] Among them, μ' is the updated mean carrier frequency of the existing target, i.e. the comparison sequence, stored in the database, σ is the standard deviation of the carrier frequency of the new input signal, i.e. the reference sequence, μ is the mean carrier frequency of the current existing target, i.e. the comparison sequence, stored in the database, span_out is the maximum extension range of the signal carrier frequency, multi is the adjustment factor, and f is the mean carrier frequency of the new input signal, i.e. the reference sequence.

[0018] Furthermore, each column of the identification matrix is ​​normalized to obtain the matrix to be identified, specifically:

[0019] Gaussian normalization is used to process the carrier frequency characteristic vector parameters of each column in the identification matrix;

[0020] For other characteristic vector parameters of each column in the recognition matrix, normalization is performed in the following manner:

[0021]

[0022] Wherein, max represents the maximum value of the kth column of the matrix composed of the reference sequence and the comparison sequence, min represents the minimum value of the kth column, X0={X0(k)|k=1,2,...,n} is the reference sequence, and n is the index representing the reference sequence; X i ={X i(k)|k=1,2,...,n}(i=1,2,...,m) is a comparison sequence, m is the number of comparison sequences, and i represents the index of the comparison sequence.

[0023] Furthermore, the Gaussian normalization is used to process the carrier frequency feature vector parameters of each column in the identification matrix, specifically:

[0024]

[0025] Where f is the mean carrier frequency of the new input signal, i.e., the reference sequence; μ is the mean carrier frequency of the current target, i.e., the comparison sequence, stored in the database; σ is the standard deviation of the carrier frequencies of the new input signal and the reference sequence; and δ is the set carrier frequency variance threshold.

[0026] Furthermore, the grey correlation analysis method is used to calculate the weighted grey correlation degree of each column in the matrix to be identified, specifically:

[0027]

[0028] Among them, ξ i (k) is the correlation coefficient, α k is the weight calculated by the entropy weight method, m is the number of comparison series, and i is the index representing the comparison series.

[0029] Furthermore, the weight α calculated by the entropy weight method is k After that, use the α filter to adjust the current weight α k Perform adaptive updates, specifically:

[0030]

[0031] Among them, α is a constant whose range is 0≤α≤1. is the entropy weight of the previous moment, α' k is the updated entropy weight.

[0032] According to some embodiments, a second solution of the present invention provides a radiation source target recognition system with adaptive weight update, which adopts the following technical solution:

[0033] A radiation source target recognition system with adaptive weight updating, comprising:

[0034] A signal acquisition module is configured to acquire a radiation source signal to be identified and select a characteristic vector to obtain a reference sequence;

[0035] The signal processing module is configured to form an identification matrix by combining the reference number series and the comparison number series, and to perform normalization processing on each column of the identification matrix to obtain a matrix to be identified;

[0036] A grey relational degree calculation module is configured to calculate the weighted grey relational degree of each column in the matrix to be identified by using a grey relational analysis method;

[0037] The target recognition module is configured to sort the calculated weighted grey correlation degrees by size, determine the comparison sequence corresponding to the largest weighted grey correlation degree, and the reference sequence is similar to the comparison sequence.

[0038] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the radiation source target recognition method with adaptive weight update as described in the first aspect above.

[0040] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0041] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the radiation source target recognition method with adaptive weight updating as described in the first aspect above are implemented.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] Based on the traditional grey correlation method, the present invention aims at the problem that the frequency dimension of frequency agility and jitter radiation source signals does not have clustering, which easily causes the target to be separated into multiple pulse trains after clustering sorting. A Gaussian normalization method is proposed to normalize the pulse parameters, and an α filter is used to update the current weights, thereby improving the estimation accuracy of the weights. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0045] Figure 1 It is a flow chart of a radiation source target identification method with adaptive weight updating in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0047] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0049] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0050] Embodiment 1

[0051] like Figure 1 As shown, this embodiment provides a radiation source target identification method with adaptive weight update. This embodiment uses the method applied to a server as an example for illustration. It can be understood that the method can also be applied to a terminal, and can also be applied to a terminal, a server, and a system, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0052] Acquire the radiation source signal to be identified, and select the characteristic vector to obtain a reference sequence;

[0053] The reference number series and the comparison number series are combined into an identification matrix, and each column of the identification matrix is ​​normalized to obtain a matrix to be identified;

[0054] The grey relational analysis method is used to calculate the weighted grey relational degree of each column in the matrix to be identified;

[0055] The calculated weighted grey correlation degrees are sorted by size, and the comparison sequence corresponding to the largest weighted grey correlation degree is determined. If the reference sequence is similar to the comparison sequence, the radiation source target is identified.

[0056] The frequency of frequency agility and jitter radar does not have the characteristics of aggregation, which easily leads to the separation of the target into multiple pulse strings after clustering and sorting, resulting in the error of identifying the radiation source target. In order to eliminate this error, the present invention proposes a Gaussian normalization method to normalize the pulse parameters. After the feature parameters are normalized, the grayscale correlation method is used to find the target with the highest matching degree for the new input data; when the entropy weight method is used to obtain the weight of each feature, the present invention uses an α filter to update the current weight, thereby improving the estimation accuracy of the weight.

[0057] The characteristic vector of the radiation source signal to be identified is selected as the reference sequence, and the characteristic vector of the radiation source signal stored in the database is selected as the comparison sequence. When the radiation source signal to be identified is input, this scheme uses the gray correlation analysis method to match and identify the reference sequence and the comparison sequence in the database.

[0058] The grey correlation analysis method must first normalize the characteristic vector parameters. Characteristic vector parameter normalization is to transform multiple characteristic vector parameters into identifiable and comparable features within (0,1). The normalized characteristic parameters include carrier frequency, PRI (pulse repetition interval), PW (pulse width), etc.

[0059] For frequency agile and jitter radar signals, the frequency dimension does not have clustering properties, which causes the target to be separated into multiple pulse trains after clustering and sorting, resulting in incorrect identification of the radiation source target. In the signal identification stage, in order to eliminate this error, Gaussian normalization is used to process the carrier frequency characteristics. The formula is:

[0060]

[0061] Where f is the carrier frequency mean of the new input signal, i.e., the reference sequence; μ is the carrier frequency mean of the existing target, i.e., the comparison sequence, stored in the database; σ is the standard deviation of the carrier frequency of the new input signal; and δ is the set carrier frequency variance threshold.

[0062] For frequency agile and jitter radar signals, their frequencies are not clustered, so a Gaussian normalization method is proposed to normalize the carrier frequency.

[0063] After the reference sequence is identified, it is fused with the comparison sequence in the database, that is, the pulse signal is fused, and the characteristic vector parameters of the comparison sequence are updated. For the carrier frequency parameters in the present invention, after the pulse signal is fused, the carrier frequency mean u and the carrier frequency standard deviation σ are updated, and the update formula is:

[0064]

[0065] Wherein, μ' is the updated mean carrier frequency of the existing target, i.e., the comparison sequence, stored in the database; σ is the standard deviation of the carrier frequency of the new input signal, i.e., the reference sequence; μ is the mean carrier frequency of the current existing target, i.e., the comparison sequence, stored in the database; span_out is the maximum extension range of the signal carrier frequency; and multi is the adjustment factor.

[0066] Because as the number of received signals increases, the parameters in the stored database need to be constantly modified according to the new signal characteristics. For example, if the average carrier frequency of the existing signal is f, then after receiving the same type of signal (assuming that the carrier frequency of the newly received signal is f2), the existing average carrier frequency f needs to be updated according to the new signal f2.

[0067] The specific steps of radiation source target identification are:

[0068] Step 1: Let X0 = {X0(k)|k = 1, 2, ... n} be the reference sequence, and n be the index representing the reference sequence; X i ={X i (k)|k=1,2,...n},i=1,2,...m is a comparison sequence, m is the number of comparison sequences, and i represents the index of the comparison sequence.

[0069] The reference sequence and the comparison sequence in the database are combined into a new matrix, and then each column is normalized. For example, here 1 X0 and m X i The row vectors can form a (m+1)xn matrix.

[0070] It can be written as follows:

[0071] The carrier frequency of the frequency-agile or jittery radar signal is normalized using formula (1), and other characteristic quantities (such as PRI, PW) can be normalized using formula (3).

[0072]

[0073] Among them, max represents the maximum value of the kth column of the matrix composed of the reference sequence and the comparison sequence, and min represents the minimum value of the kth column.

[0074] Step 2: Use grey relational analysis to calculate the weighted grey relational degree γ i , the calculation formula is:

[0075]

[0076] Among them, ξ i (k) is the correlation coefficient, α k is the weight calculated using the entropy weight method.

[0077] Specifically, in order to k Smoothing is also done to use the entropy weight of the previous moment (Indicates that the last recognition process obtained α k ) information, and use the entropy weight method to calculate the α in this recognition process k After that, use the α filter to adjust the current weight α k Perform adaptive update, the specific expression is as follows:

[0078]

[0079] Among them, α is a constant set artificially and its range is 0≤α≤1. is the entropy weight of the previous moment, α' k is the updated entropy weight.

[0080] The entropy weight method is used to obtain the weights, and the α filter is used to filter the weights, so as to achieve adaptive update of the weights.

[0081] Step 3: Sort the gray correlation by size and find the largest gray correlation γ j =max{γ i , i=1,2,...m}, then the reference sequence X0 and the comparison sequence X j Similarly, the signal recognition is completed.

[0082] Embodiment 2

[0083] This embodiment provides a radiation source target recognition system with adaptive weight update, including:

[0084] A signal acquisition module is configured to acquire a radiation source signal to be identified and select a characteristic vector to obtain a reference sequence;

[0085] The signal processing module is configured to form an identification matrix by combining the reference number series and the comparison number series, and to perform normalization processing on each column of the identification matrix to obtain a matrix to be identified;

[0086] A grey relational degree calculation module is configured to calculate the weighted grey relational degree of each column in the matrix to be identified by using a grey relational analysis method;

[0087] The target recognition module is configured to sort the calculated weighted grey correlation degrees by size, determine the comparison sequence corresponding to the largest weighted grey correlation degree, and the reference sequence is similar to the comparison sequence.

[0088] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0089] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0091] Embodiment 3

[0092] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the radiation source target identification method with adaptive weight update as described in the first embodiment above are implemented.

[0093] Embodiment 4

[0094] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the radiation source target identification method with adaptive weight update as described in the first embodiment are implemented.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0100] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A radiation source target recognition method with adaptive weight updating, characterized in that: include: Acquire the radiation source signal to be identified, and select the characteristic vector to obtain a reference sequence; The reference number series and the comparison number series are combined into an identification matrix, and each column of the identification matrix is ​​normalized to obtain a matrix to be identified; The grey relational analysis method is used to calculate the weighted grey relational degree of each column in the matrix to be identified; The calculated weighted grey correlation degrees are sorted by size, and the comparison sequence corresponding to the largest weighted grey correlation degree is determined. If the reference sequence is similar to the comparison sequence, the radiation source target is identified.

2. The radiation source target recognition method with adaptive weight updating according to claim 1, characterized in that: The reference sequence and the pulse signal of the comparison sequence are fused to obtain a recognition matrix; After the pulse signals are fused, the characteristic vector parameters of the comparison sequence and the reference sequence are updated.

3. The radiation source target recognition method with adaptive weight updating as claimed in claim 2, characterized in that: The updating of the characteristic vector parameters of the comparison sequence and the reference sequence is specifically as follows: Among them, μ' is the updated mean carrier frequency of the existing target, i.e. the comparison sequence, stored in the database, σ is the standard deviation of the carrier frequency of the new input signal, i.e. the reference sequence, μ is the mean carrier frequency of the current existing target, i.e. the comparison sequence, stored in the database, a is the number of times of the current recognition process, span_out is the maximum extension range of the signal carrier frequency, multi is the adjustment factor, and f is the mean carrier frequency of the new input signal, i.e. the reference sequence.

4. The radiation source target recognition method with adaptive weight updating according to claim 1, characterized in that: The normalization process is performed on each column of the identification matrix to obtain the matrix to be identified, which is specifically: Gaussian normalization is used to process the carrier frequency characteristic vector parameters of each column in the identification matrix; For other characteristic vector parameters of each column in the recognition matrix, normalization is performed in the following manner: Wherein, max represents the maximum value of the kth column of the matrix composed of the reference sequence and the comparison sequence, min represents the minimum value of the kth column, X0={X0(k)|k=1,2,...,n} is the reference sequence, and n is the index representing the reference sequence; X i ={X i (k)|k=1,2,...,n}(i=1,2,...,m) is a comparison sequence, m is the number of comparison sequences, and i represents the index of the comparison sequence.

5. The radiation source target recognition method with adaptive weight updating as claimed in claim 4, characterized in that: The Gaussian normalization is used to process the carrier frequency characteristic vector parameters of each column in the identification matrix, specifically: Where f is the mean carrier frequency of the new input signal, i.e., the reference sequence; μ is the mean carrier frequency of the current target, i.e., the comparison sequence, stored in the database; σ is the standard deviation of the carrier frequencies of the new input signal and the reference sequence; and δ is the set carrier frequency variance threshold.

6. The radiation source target recognition method with adaptive weight updating according to claim 1, characterized in that: The grey correlation analysis method is used to calculate the weighted grey correlation degree of each column in the matrix to be identified, specifically: Among them, ξ i (k) is the correlation coefficient, α k is the weight calculated by the entropy weight method, m is the number of comparison series, and i is the index representing the comparison series.

7. The radiation source target recognition method with adaptive weight updating according to claim 6, characterized in that: The weight α calculated by the entropy weight method k After that, use the α filter to adjust the current weight α k Perform adaptive updates, specifically: Among them, α is a constant whose range is 0≤α≤1. is the entropy weight of the previous moment, α' k is the updated entropy weight.

8. A radiation source target recognition system with adaptive weight update, characterized in that: include: A signal acquisition module is configured to acquire a radiation source signal to be identified and select a characteristic vector to obtain a reference sequence; The signal processing module is configured to form an identification matrix by combining the reference number series and the comparison number series, and to perform normalization processing on each column of the identification matrix to obtain a matrix to be identified; A grey relational degree calculation module is configured to calculate the weighted grey relational degree of each column in the matrix to be identified by using a grey relational analysis method; The target recognition module is configured to sort the calculated weighted grey correlation degrees by size, determine the comparison sequence corresponding to the largest weighted grey correlation degree, and the reference sequence is similar to the comparison sequence.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the radiation source target identification method with adaptive weight update as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the radiation source target identification method with adaptive weight update as described in any one of claims 1-7 are implemented.

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