Environment signal blocking method based on feature information entropy

Abstract

The invention relates to an environmental signal blocking method based on characteristic information entropy, which comprises the following steps of: blocking partial environmental signal radiation source signals through a wave limiter according to frequency parameter information, comparing and matching signals detected in a complex electromagnetic environment with a radiation source identification database, and identifying the radiation source identification database; blocking non-combat signals matched with information in a database, clustering unrecognized radiation source signals based on carrier frequency, pulse width and arrival angle parameter information, calculating characteristic information entropy of pulse width parameters according to a clustering result, comparing the characteristic information entropy of the pulse width parameters with a set threshold value, and determining whether the radiation source signals cannot be recognized or not according to the characteristic information entropy of the pulse width parameters. The radiation source signal composition condition of the pre-sorting clustering result is judged, so that the number of radiation sources in different categories is estimated; in a word, the method has the advantages that the algorithm processing pressure can be relieved, the two-dimensional parameter joint clustering accuracy is improved, and environmental signals can be quickly and effectively filtered out.

Description

Technical Field

[0001] The invention belongs to the technical field of electronic warfare reconnaissance, and in particular relates to an environmental signal blocking method based on characteristic information entropy. Background Art

[0002] The signals received in complex electromagnetic environments are mixed with a large number of non-combat signals. The process of blocking and filtering these useless signals is equivalent to the process of classifying combat signals and non-combat signals based on their respective characteristics. The electromagnetic frequency band, function, power, signal waveform and modulation method of combat signals and non-combat signals can be analyzed and compared, and a combat / non-combat radiation source signal identification database can be established. According to the database information, the signals received in complex electromagnetic environments can be roughly sorted. However, due to the limited information in the database, the rough sorting cannot completely filter out non-combat signals.

[0003] However, in a high-density electromagnetic environment, it is very important to quickly and efficiently block the non-combat radiation source signals received by the reconnaissance. However, if the existing sorting and identification link adds an environmental signal reprocessing link, it will significantly increase the computational complexity and generate many unnecessary redundant calculations, which cannot meet the needs of electronic warfare reconnaissance scenarios. Therefore, a method for blocking environmental signals based on characteristic information entropy is needed. By combining database information, a method for blocking non-combat signals through a pre-sorting clustering algorithm can be used to solve the existing shortcomings of not being able to completely filter out non-combat signals, having many redundant calculations and not being able to meet the needs of electronic warfare reconnaissance. Summary of the invention

[0004] In view of this, the present invention proposes an environmental signal blocking method based on characteristic information entropy, which is applied to the field of electronic warfare reconnaissance technology. It can block non-combat signals through a pre-sorting clustering algorithm, thereby solving the existing technical problems of being unable to completely filter out non-combat signals, having many redundant calculations and being unable to meet the needs of electronic warfare reconnaissance.

[0005] In order to achieve the above technical objectives, the specific technical solutions adopted by the present invention are:

[0006] An environmental signal blocking method based on characteristic information entropy comprises the following steps:

[0007] S1. Block some environmental signal radiation source signals through a wave limiter according to the frequency parameter information;

[0008] S2. Compare and match the signals received in complex electromagnetic environments with the radiation source identification database, and block non-combat signals that match the information in the database;

[0009] S3, clustering the radiation source signals that cannot be identified based on the carrier frequency, pulse width and arrival angle parameter information, and calculating the characteristic information entropy of the pulse width parameter according to the clustering results;

[0010] S4. Compare the characteristic information entropy of the pulse width parameter with the set threshold to determine the composition of the radiation source signal of the pre-sorting clustering result, thereby estimating the number of radiation sources in different categories.

[0011] Furthermore, some of the environmental signal radiation source signals targeted in step S1 are low-frequency non-combat signals, including shortwave communications and data communications.

[0012] Furthermore, in step S2, the radiation source identification database is a radar database, and frequency and pulse width are selected as blocking parameters. The upper and lower limits of the selected blocking parameters are set, and the blocking interval is the intersection of the frequency parameter and pulse width parameter ranges. The parameters in the intersection are matched with the environmental signal parameter information in the database. If there is a match, the non-combat signal is blocked according to the matching result.

[0013] Furthermore, in step S2, the successful matching results are stored and counted, and false analysis is performed, and it is determined whether to update the database based on the results.

[0014] Furthermore, in step S3, according to the parameter information of the three dimensions of the arrival angle, pulse width and frequency of the radiation source signal, any two are selected for two-dimensional parameter clustering, data with the same characteristics are classified into one category, and then adjacent clusters are merged, and the pulse width parameter feature information entropy is calculated according to the merged result.

[0015] Furthermore, the clustering process in step S3 includes:

[0016] Initialize clustering: M = 1, Center (1) = (X 1 ,Y 1 ), Index 1 =1, where M represents the number of clusters obtained by clustering, X and Y represent the parameter types used for clustering, Center(i) represents the cluster center of the i-th class, and Index i Represents the parameter index of the i-th category, i=1,…M;

[0017] Starting from the second radiation source data point on the X and Y two-dimensional planes, if Center(i)-(X n ,Y n )|≤dis_Thr, then the nth radiation source data point (X n ,Y n ) belongs to the i-th category, and (X n ,Y n) is added to the Index of the i-th category i Where dis_Thr represents the distance threshold of clustering. If the distance threshold condition is not met, the nth radiation source data point (X n ,Y n ) belongs to the new class: M = M + 1, Index M =n.

[0018] Furthermore, the process of merging adjacent cluster rows in step S3 includes:

[0019] Initialize the merged category, N=1, NIndex 1 =Index 1 , where N represents the number of categories after merging, NIndex 1 Represents the data index of the nth category after merging, n=1,…,N;

[0020] Then from the Index 2 Start traversing all Indexes i , if the following conditions are met:

[0021]

[0022] Then the Index of the i-th category i Index added to NIndex N If not satisfied: N = N + 1, NIndex N =Index i , where X N and Y N Represents the coordinate value of the data point of the nth cluster center, X i and Y i Indicates the Index of the i-th category i The coordinate value of the data point corresponding to the index, dis_thr represents the distance threshold between the cluster center and the data to be merged.

[0023] Furthermore, the definition of feature information entropy in step S3 is:

[0024]

[0025] Where m is the number of feature statistics categories; p(x i ) represents the probability density function. H(X) represents the PW feature information entropy.

[0026] Furthermore, the threshold value set in step S4 is threshold 1. If the pulse width parameter characteristic information entropy H(X) is greater than threshold 1, the arrival time difference parameter information entropy is further calculated. If the time difference parameter characteristic information entropy is less than threshold 1, it is considered that a multi-radiation source sequence is sent to the main sorting. If it is greater than threshold 1, the current sequence is a single radiation source combat signal.

[0027] Furthermore, in step S4, if the characteristic information entropy of the pulse width parameter is less than the threshold 1, the pulse sequence with the most pulse width parameter values ​​is further extracted, and the time difference parameter characteristic information entropy of the pulse sequence is calculated. If the time difference parameter characteristic information entropy is less than the threshold 1, the pulse sequence is mainly sorted, otherwise it is determined as a non-combat signal and filtered out.

[0028] By adopting the above technical solution, the present invention can also bring the following beneficial effects:

[0029] 1. The present invention mentions an environmental signal blocking method based on characteristic information entropy, which can quickly and efficiently block the non-combat radiation source signals received in a high-density complex electromagnetic environment, avoid processing the environmental signals in the sorting and identification link, and thus reduce redundant calculations, effectively improve the efficiency of radiation source signal sorting in a complex electromagnetic environment, and can be used in the field of radar electronic warfare signal sorting to perform pre-sorting radiation source signal composition judgment, facilitate subsequent main sorting to process data, improve the overall sorting efficiency, and facilitate the processing of radar electronic warfare signals.

[0030] 2. The present invention mentions an environmental signal blocking method based on characteristic information entropy, which quickly filters out environmental signals through three steps: radiation source database screening, two-dimensional joint clustering, and calculation of characteristic information entropy. First, database screening can quickly and effectively filter out some typical non-combat radiation source signals to reduce the processing pressure of subsequent clustering algorithms. Then, through two-dimensional parameter joint clustering, it can quickly cluster under the radiation source sequence at one time, and use adjacent clusters to merge to improve clustering accuracy. At the same time, characteristic information entropy is introduced to calculate and characterize the degree of confusion of pulse width parameters and arrival time difference parameters, providing accurate judgment basis for the composition form analysis of radiation source signals, and quickly, accurately and effectively filtering out environmental signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1It is a flowchart of an environmental signal blocking method based on characteristic information entropy in a specific implementation manner of the present invention;

[0033] Figure 2 This is a schematic diagram of the clustering result of radiation source signals in a specific implementation manner of the present invention;

[0034] Figure 3 It is a schematic diagram of the frequency statistics of pulse width parameter values ​​and characteristic information entropy values ​​in a specific implementation manner of the present invention;

[0035] Figure 4 The DTOA statistical frequency of a single radiation source in a specific embodiment of the present invention; DETAILED DESCRIPTION

[0037] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0038] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0039] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present invention, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0040] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. The drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0041] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0042] Example 1

[0043] In one embodiment of the present invention, Figure 1 As shown, the present invention mentions an environmental signal blocking method based on characteristic information entropy, the method comprising:

[0044] Step 1: Based on the frequency parameter information, some environmental signal radiation source signals are blocked by a wave limiter. This step mainly targets non-combat signals such as shortwave communication, data communication and other low-frequency non-combat signals;

[0045] Step 2: Database matching blocking, select frequency (RF) and pulse width (PW) as blocking parameters, set the upper and lower limits of the selected blocking parameters, and the blocking interval is the intersection of the RF parameter and PW parameter range. If the parameters in the intersection match the environmental signal parameter information in the database, if they match, store the matching results for statistics, perform false analysis, and determine whether to update the database based on the results;

[0046] Step 3: According to the three-dimensional parameter information of the radiation source signal, namely, angle of arrival (DOA), pulse width and frequency, any two parameters are selected for two-dimensional parameter joint clustering. First, clustering is initialized: M = 1, Center (1) = (X 1 ,Y 1 ), Index 1 = 1. Where M represents the number of clusters obtained by clustering, X and Y represent the parameter types used for clustering, Center(i) represents the cluster center of the i-th class, Index i represents the parameter index of the i-th category, i=1,…M; then start traversing from the second radiation source data point on the X and Y two-dimensional planes, if Center(i)-(X n ,Y n )|≤dis_Thr, then the nth radiation source data point (X n ,Y n ) belongs to the i-th category, and (X n ,Y n ) is added to the Index of the i-th category i Where dis_Thr represents the distance threshold of clustering. If the distance threshold condition is not met, the nth radiation source data point (X n ,Y n ) belongs to the new class: M = M + 1, Index M =n.

[0047] Step 4: Merge adjacent clusters. First, initialize the merged categories, N = 1, NIndex 1 =Index 1 Among them, N represents the number of categories after merging, NIndex 1 Represents the data index of the nth category after merging, n=1,…,N. Then from the Index 2 Start traversing all Indexes i , if the following conditions are met:

[0048]

[0049] Then the Index of the i-th category i Index added to NIndex N If not satisfied: N = N + 1, NIndex N =Index i Among them, X N and Y N Represents the coordinate value of the data point of the nth cluster center, X i and Y i Indicates the Index of the i-th category i The coordinate value of the data point corresponding to the index, dis_thr represents the distance threshold between the cluster center and the data to be merged.

[0050] Step 5: Calculate the characteristic information entropy of the pulse width parameter. The definition of characteristic information entropy is:

[0051]

[0052] Where m is the number of feature statistics categories; p(x i ) represents the probability density function.

[0053] Step 6: If the PW feature information entropy is greater than threshold 1, the arrival time difference (DTOA) parameter information entropy is further calculated. If the DTOA feature information entropy is less than threshold 1, it is considered to be a multi-radiator sequence sent to the main sorting. If it is greater than threshold 1, the current sequence is a single-radiator combat signal.

[0054] Step 7: If the PW characteristic information entropy is less than threshold 1, further extract the pulse sequence with the most PW values ​​and calculate the DTOA characteristic information entropy of the pulse sequence. If the DTOA characteristic information entropy is less than threshold 1, perform main sorting on the pulse sequence; otherwise, determine it as a non-combat signal and filter it out.

[0055] In view of the environmental signal blocking method based on characteristic information entropy mentioned in the present invention, six radar radiation source signals and three communication signals are set for simulated communication, wherein the six radar signals include a weather radar signal and a navigation radar signal which is a non-combat radar. Among the three communication signals, a shortwave communication signal whose working frequency band is in the VHF band is included. Data analysis is performed on the above-mentioned simulated communication results, and the two non-combat radar signals are blocked by database matching. For a shortwave communication signal whose working frequency band is in the VHF band, it is blocked by limiting the wave at the RF end. The remaining six radiation source signals need to be classified by clustering algorithm and characteristic information entropy method. The analysis results are as follows: Figures 2 to 4 As shown, the clustering results of the remaining six radiation source signals, the pulse width (PW) characteristic information entropy value, the DTOA statistical frequency of a single radiation source and the DTOA statistical frequency after the two radiation sources are interleaved are obtained respectively. The pulse width (PW) characteristic information entropy value is used to determine whether the clustered cluster contains a radar radiation source signal or a common environmental signal. The DTOA statistical frequency of a single radiation source and the DTOA statistical frequency after the two radiation sources are interleaved are used to determine whether the clustered cluster contains only one radar radiation source signal, so that the environmental signal can be quickly and accurately filtered out.

[0056] In summary, the present invention quickly and effectively filters out some typical non-combat radiation source signals through database screening to reduce the processing pressure of the subsequent clustering algorithm, and then uses two-dimensional parameter joint clustering to traverse the radiation source sequence quickly and cluster at one time, and uses adjacent clusters to merge to improve clustering accuracy. Finally, the characteristic information entropy is introduced to calculate and characterize the degree of confusion of the pulse width parameter and the arrival time difference parameter, providing an accurate judgment basis for the composition form analysis of the radiation source signal, thereby quickly, accurately and effectively filtering out the environmental signal.

[0057] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for blocking environmental signals based on characteristic information entropy, characterized in that: The following steps are involved: S1. Block some environmental signal radiation source signals through a wave limiter according to the frequency parameter information; S2. Compare and match the signals received in complex electromagnetic environments with the radiation source identification database, and block non-combat signals that match the information in the database; S3, clustering the radiation source signals that cannot be identified based on the carrier frequency, pulse width and arrival angle parameter information, and calculating the characteristic information entropy of the pulse width parameter according to the clustering results; S4. Compare the characteristic information entropy of the pulse width parameter with the set threshold to determine the composition of the radiation source signal of the pre-sorting clustering result, thereby estimating the number of radiation sources in different categories.

2. The environmental signal blocking method based on characteristic information entropy according to claim 1, characterized in that: The partial environmental signal radiation source signals targeted in step S1 are low-frequency non-combat signals, including shortwave communications and data communications.

3. The environmental signal blocking method based on characteristic information entropy according to claim 2, characterized in that: In step S2, the radiation source identification database is a radar database, and frequency and pulse width are selected as blocking parameters. The upper and lower limits of the selected blocking parameters are set, and the blocking interval is the intersection of the frequency parameter and pulse width parameter range. The parameters in the intersection are matched with the environmental signal parameter information in the database. If there is a match, non-combat signals are blocked according to the matching results.

4. The environmental signal blocking method based on characteristic information entropy according to claim 3, characterized in that: In step S2, the successful matching results are stored and counted, and false analysis is performed, and it is determined whether to update the database based on the results.

5. The method for blocking environmental signals based on characteristic information entropy according to claim 4, characterized in that: In step S3, two parameters of the three dimensions of the angle of arrival, pulse width and frequency of the radiation source signal are randomly selected for two-dimensional parameter clustering, data with the same characteristics are classified into one category, and then adjacent clusters are merged, and the pulse width parameter characteristic information entropy is calculated based on the merged result.

6. The method for blocking environmental signals based on characteristic information entropy according to claim 5, characterized in that: The clustering process in step S3 includes: Initialize clustering: M = 1, Center(1) = (X1, Y1), Index1 = 1, where M represents the number of clusters obtained by clustering, X and Y represent the parameter types used for clustering, Center(i) represents the cluster center of the i-th class, and Index i Represents the parameter index of the i-th category, i=1,…M; Starting from the second radiation source data point on the X and Y two-dimensional planes, if Center(i)-(X n ,Y n )≤dis_Thr, then the nth radiation source data point (X n ,Y n ) belongs to the i-th category, and (X n ,Y n ) is added to the Index of the i-th category i Where dis_Thr represents the distance threshold of clustering. If the distance threshold condition is not met, the nth radiation source data point (X n ,Y n ) belongs to the new class: M = M + 1, Index M =n.

7. The method for blocking environmental signals based on characteristic information entropy according to claim 6, characterized in that: The process of merging adjacent cluster rows in step S3 includes: Initialize the merged categories, N=1, NIndex1=Index1, where N represents the number of categories after merging, NIndex1 represents the data index of the nth category after merging, n=1,…,N; Then traverse all Indexes starting from Index2 i , if the following conditions are met: Then the Index of the i-th category i Index added to NIndex N If not satisfied: N = N + 1, NIndex N =Index i , where X N and Y N Represents the coordinate value of the data point of the nth cluster center, X i and Y i Indicates the Index of the i-th category i The coordinate value of the data point corresponding to the index, dis_thr represents the distance threshold between the cluster center and the data to be merged.

8. The method for blocking environmental signals based on characteristic information entropy according to claim 7, characterized in that: The definition of the characteristic information entropy in step S3 is: Where m is the number of feature statistics categories; p(x i ) represents the probability density function, and H(X) represents the PW feature information entropy.

9. The method for blocking environmental signals based on characteristic information entropy according to claim 8, characterized in that: The threshold value set in step S4 is threshold 1. If the pulse width parameter characteristic information entropy H(X) is greater than threshold 1, the arrival time difference parameter information entropy is further calculated. If the time difference parameter characteristic information entropy is less than threshold 1, it is considered that a multi-radiation source sequence is sent to the main sorting. If it is greater than threshold 1, the current sequence is a single radiation source combat signal.

10. The method for blocking environmental signals based on characteristic information entropy according to claim 9, characterized in that: In step S4, if the characteristic information entropy of the pulse width parameter is less than threshold 1, the pulse sequence with the most pulse width parameter values ​​is further extracted, and the time difference parameter characteristic information entropy of the pulse sequence is calculated. If the time difference parameter characteristic information entropy is less than threshold 1, the pulse sequence is mainly sorted, otherwise it is determined as a non-combat signal and filtered out.

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