Azimuth clustering method and device based on dop, and medium

Abstract

The application discloses a bearing clustering method and device based on a heavy frequency distribution entropy and a medium, belongs to the field of electronic reconnaissance of electronic warfare signal processing, and comprises the following steps: introducing the heavy frequency distribution entropy to measure the clustering results of different bearing thresholds, then selecting an optimal bearing threshold according to the change of the heavy frequency entropy to cluster pulses, and finally selecting the optimal bearing threshold to cluster the pulses. The application can effectively improve the sorting capability for densely distributed radiation source targets.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electronic reconnaissance of electronic warfare signal processing, and more particularly to a bearing clustering method based on re-frequent distribution entropy, equipment and medium. BACKGROUND

[0002] Signal sorting is the core of electronic reconnaissance, and its essence is to complete the de-interleaving of pulses by clustering the bearing and electromagnetic parameters of the pulses. Bearing clustering is to sort pulses by taking advantage of the characteristic that the spatial position of a radiation source will not change significantly in a short time. Its role becomes increasingly important in the context of increasingly serious overlap of electromagnetic parameters of radiation sources. Traditional bearing-based clustering algorithms are generally based on the direction-finding accuracy of electronic warfare systems and need to consider the impact of differences in correction at different frequencies. A fixed bearing threshold that can cover most scenarios is usually selected for clustering, which makes the algorithm unable to adaptively adjust the threshold according to the signal scenario, reducing the adaptability of the algorithm to densely distributed target scenarios. SUMMARY

[0003] The present application aims to overcome the shortcomings of the prior art and provide a bearing clustering method based on re-frequent distribution entropy, equipment and medium, which can achieve better clustering effect for densely distributed radiation source targets.

[0004] The purpose of the present application is achieved by the following scheme:

[0005] A bearing clustering method based on re-frequent distribution entropy, comprising the following steps:

[0006] Step 1: Determine the bearing threshold range according to the system direction-finding accuracy, set the system direction-finding accuracy as σ, and select the bearing step value as Aoa Step ;

[0007] Step 2: Equally divide the pulse bearing with Aoa Step as the bearing resolution unit, and count the number of pulses belonging to the corresponding equal division;

[0008] Step 3: According to the equal division bearing obtained in Step 2, extract the pulses at the selected equal division position. If the number of extracted pulses is greater than or equal to the clustering pulse number threshold GrpPulseNum Gate , it is considered that the group of clustering pulses is valid, otherwise it is considered invalid;

[0009] Step 4: Repeat Step 3 to extract other bearing clustering group pulses except the selected position until all valid bearing clustering pulse groups DoaCluster i , i = 1, 2, …, M, M is the number of bearing clustering result groups;

[0010]

[0011] P is the total number of processing pulses in formula (1);

[0012] The fifth step: the azimuth clustering result of the fourth step is clustered to obtain PriCluster ij , i = 1, 2, …, M, j = 1, 2, …, N; PriCluster ij is the jth clustering result of the ith azimuth clustering result after the clustering, if the number of pulses contained in the clustering PriCluster ij is K ij , then the corresponding repetition frequency probability P ij is as follows:

[0013] P ij = K ij / N (2)

[0014] The repetition frequency distribution entropy PE is calculated according to formula (3):

[0015]

[0016] The sixth step: repeating the third step to the fifth step with Aoa Gate = i x Aoa Step as the azimuth threshold, i = 1, 2, …, S, and obtaining the corresponding PE i , i = 1, 2, …, S.

[0017] The seventh step: the azimuth threshold AoaGate MinPritropy corresponding to the minimum repetition frequency entropy is counted, and the pulses are azimuth clustered with the azimuth threshold.

[0018] Further, in the first step, the azimuth threshold range is determined according to the system direction finding accuracy, including the steps of setting the azimuth threshold range to (0, 3σ] with Aoa Step as the minimum step value.

[0019] Further, in the second step, the pulses are equally divided in azimuth with Aoa Step as the azimuth resolution unit, including the steps of dividing the pulse azimuth into corresponding equal parts from 0° to 360°.

[0020] Further, in the third step, the pulses at selected equal division positions are extracted according to the equal division azimuth obtained in the second step, including the steps of equally dividing the azimuth Aoa Center with the maximum pulse number as the center, and extracting the pulses at the equal division positions of Aoa Center , Aoa Center ± Aoa Step .

[0021] Further, in the third step, after extracting the pulses of the selected equal-division position, the step of clearing the extracted number of equal-division position pulses is included.

[0022] Further, in the fifth step, the PE reflects the uniformity of the re- frequency distribution of the azimuth clustering group, the greater the PE, the more disordered the re-frequency distribution, and the smaller the PE, the more uniform the re- frequency distribution.

[0023] Further, M and N are positive integers.

[0024] Further, in the sixth step, S = 3σ / Aoa Step .

[0025] A computer device comprising a processor and a memory, the memory storing a computer program, when the computer program is loaded and executed by the processor, the method of any one of the above.

[0026] A readable storage medium, in which a computer program is stored, the computer program is loaded and executed by a processor, the method of any one of the above.

[0027] The beneficial effects of the present application include:

[0028] The technical scheme of the present application can obtain better clustering effect on densely distributed radiation source targets.

[0029] The adaptive azimuth clustering method based on re-frequency distribution entropy proposed by the technical scheme of the embodiment of the present application, without changing the existing hardware architecture of the electronic warfare system, by introducing the concept of re-frequency distribution entropy, the clustering results of different azimuth thresholds are evaluated, and then the optimal azimuth threshold is selected for clustering, which better solves the technical problem of poor processing effect of the traditional azimuth clustering method on dense targets. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or the prior art description will be briefly introduced below, and obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0031] Figure 1 The flow chart of the adaptive azimuth clustering processing based on re- frequency distribution entropy of the embodiment of the present application;

[0032] Figure 2 The pulse azimuth equal-division schematic diagram of the embodiment of the present application;

[0033] Figure 3This is a screenshot of the actual full-pulse data from an embodiment of the present invention;

[0034] Figure 4 This is a graph showing the changes in repetition rate distribution entropy and azimuth threshold according to an embodiment of the present invention.

[0035] Figure 5 This is the actual data location clustering result of an embodiment of the present invention. Detailed Implementation

[0036] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0037] To address the technical problem that traditional azimuth clustering algorithms, due to their fixed thresholds, cannot correctly sort densely distributed targets, this invention proposes an adaptive azimuth clustering method based on repetition frequency distribution entropy to process pulses. The concept of repetition frequency distribution entropy (a term defined in this invention) is introduced to measure the clustering results at different azimuth thresholds. Then, the optimal azimuth threshold is selected to cluster pulses based on the change in repetition frequency entropy (i.e., repetition frequency distribution entropy). Finally, the optimal azimuth threshold is selected for pulse clustering. Actual data verification shows that the adaptive azimuth clustering method based on repetition frequency distribution entropy can achieve good clustering results for densely distributed radiation source targets. This invention, without changing the existing electronic warfare system hardware architecture, effectively improves the sorting capability for densely distributed radiation source targets by improving and upgrading the azimuth clustering algorithm model, resulting in significant economic and military benefits.

[0038] In the specific inventive concept, the adaptive azimuth clustering method based on repetition frequency distribution entropy processes signals with fixed repetition frequency, uneven repetition frequency, or variable repetition frequency. First, the concept of repetition frequency distribution entropy is defined. Entropy is a parameter in thermodynamics characterizing the state of matter, and its physical meaning is a measure of the degree of disorder in a system. This invention defines the concept of repetition frequency distribution entropy (PriEntropy) to reflect the quality of pulse clustering results. Then, the repetition frequency entropy is used to evaluate the clustering results of all candidate azimuth thresholds. The optimal azimuth threshold is selected based on the evaluation results, and finally, the optimal azimuth threshold is used to cluster the pulses. The processing flow is as follows: Figure 1 As shown, the implementation steps are as follows:

[0039] Step 1: Determine the azimuth threshold range based on the system's direction-finding accuracy: Let the system's direction-finding accuracy be σ, and select the azimuth step value as Aoa. Step Then use Aoa Step Set the azimuth threshold range to (0, 3σ] for the minimum step value;

[0040] Step 2: Using Aoa StepThe azimuth resolution unit divides the 0°–360° range into equal parts, and counts the number of pulses belonging to each corresponding part. Figure 2 This is a schematic diagram showing the equal division of the pulse azimuth.

[0041] Step 3: Based on the equidistant orientation obtained in Step 2, divide the orientation Aoa into equal parts with the equidistant position of the maximum pulse count as the center. Center Extract the center and divide the orientation Aoa equally. Center and Aoa Center ±Aoa Step Pulses at equally divided positions, see Figure 1 As shown. The number of extracted pulses at equal division positions is then reset to zero. If the number of extracted pulses is greater than or equal to the clustering pulse count threshold GrpPulseNum... Gate If the result is positive, the clustering pulse is considered valid; otherwise, it is considered invalid.

[0042] Step 4: Repeat step 3 to extract pulses from other azimuth clusters until all valid azimuth cluster pulse groups (DoaCluster) are obtained. i , i = 1, 2, ..., M, where M is the number of directional clustering result groups.

[0043]

[0044] In equation (1), P is the total number of processing pulses.

[0045] Step 5: Perform repetition clustering on the directional clustering results from Step 4 to obtain the PriCluster. ij , i=1,2,…,M, j=1,2,…,N. PriCluster ij For the j-th clustering result after performing repetition clustering on the i-th orientation clustering result, if the clustering is PriCluster ij The number of pulses included is K ij Then the corresponding repetition probability P ij As shown in the following formula:

[0046] P ij =K ij / N (2)

[0047] The repetition frequency distribution entropy PE is calculated according to equation (3).

[0048]

[0049] PE reflects the uniformity of the repetition frequency distribution in each direction of the cluster. The larger the PE, the more disordered the repetition frequency distribution; the smaller the PE, the more uniform the repetition frequency distribution.

[0050] Step 6: Using Aoa Gate =i×Aoa StepRepeat the third to fifth steps for the azimuth threshold, i = 1, 2, …, S, where S = 3σ / Aoa Step , and the corresponding PE i , i = 1, 2, …, S.

[0051] Step 7: Calculate the minimum repetition frequency entropy corresponding to the azimuth threshold AoaGate MinPritropy , and cluster the pulses in the azimuth direction according to the azimuth threshold.

[0052] Figure 3 The full pulse screenshot of the actual data of the embodiment of the application.

[0053] Figure 4 The repetition frequency distribution entropy and azimuth threshold change graph of the embodiment of the application.

[0054] Figure 5 The azimuth clustering result of the actual data of the embodiment of the application.

[0055] The full pulse data received by the actual application is taken as the processing object, and the repetition frequency distribution entropy of the full pulse is calculated. Figure 3 It can be seen that the full pulse contains about four azimuth target signal pulses, and the frequency and pulse width parameters are basically consistent, wherein three target azimuths are relatively close and less than the target azimuth sorting threshold. The conventional azimuth clustering method cannot accurately distinguish the targets. The adaptive azimuth clustering method provided by the technical scheme of the embodiment of the application is used to cluster the pulses in the azimuth direction, and the azimuth step value is selected as 0.125°, as shown in Figure 4 The repetition frequency distribution entropy and azimuth change graph. It can be seen that when the azimuth threshold is 0.375°, the repetition frequency distribution entropy reaches the minimum value. As shown in Figure 5 The processing result of the adaptive azimuth clustering method based on the repetition frequency distribution entropy is shown, and it can be seen from the graph that the method processes four signals (one repetition frequency difference signal and three repetition frequency fixed signals), and the method achieves good clustering effect on the densely distributed targets.

[0056] It should be noted that the following embodiments can be combined and / or extended, replaced, in any logical manner within the protection scope defined in the claims of the application, for example, the disclosed technical principles, disclosed technical features or implied disclosed technical features.

[0057] Embodiment 1

[0058] An azimuth clustering method based on repetition frequency distribution entropy, comprising the following steps:

[0059] Step 1: Determine the azimuth threshold range according to the system direction finding accuracy, set the system direction finding accuracy as σ, and select the azimuth step value as Aoa Step ;

[0060] Second step: divide the pulse azimuth equally with Aoa Step and count the number of pulses belonging to the corresponding equal division;

[0061] Third step: extract the pulses at the selected equal division position according to the equal division azimuth obtained in the second step, if the number of extracted pulses is greater than or equal to the clustering pulse number threshold GrpPulseNum Gate , then consider that the group of clustering pulses is valid, otherwise consider it invalid;

[0062] Fourth step: repeat the third step to extract other azimuth clustering group pulses except the selected position until all valid azimuth clustering pulse groups DoaCluster i are obtained, i = 1, 2, …, M, and M is the number of azimuth clustering result groups;

[0063]

[0064] In formula (1), P is the total number of processed pulses;

[0065] Fifth step: perform PRF clustering on the azimuth clustering results of the fourth step to obtain PriCluster ij , i = 1, 2, …, M, and j = 1, 2, …, N; PriCluster ij is the jth clustering result after PRF clustering of the ith azimuth clustering result, if the number of pulses contained in the clustering PriCluster ij is K ij , then the corresponding PRF probability P ij is as follows:

[0066] P ij = K ij / N (2)

[0067] The PRF distribution entropy PE is calculated according to formula (3):

[0068]

[0069] Sixth step: repeat the third to fifth steps with Aoa Gate = i x Aoa Step as the azimuth threshold, i = 1, 2, …, S, and obtain the corresponding PE i , i = 1, 2, …, S.

[0070] Seventh step: count the azimuth threshold AoaGate MinPritropy corresponding to the minimum PRF entropy, and perform azimuth clustering on the pulses with the azimuth threshold.

[0071] Further, in the first step, the step of determining the azimuth threshold range according to the system direction-finding accuracy includes the step of setting the azimuth threshold range as (0, 3σ] with Aoa Step as the minimum step value.

[0072] Further, in the second step, the step of equally dividing the pulse azimuth with Aoa Step includes the step of dividing the pulse azimuth into corresponding equal parts from 0° to 360°.

[0073] Further, in the third step, the step of extracting the pulses at the selected equal-azimuth positions according to the equally divided azimuth obtained in the second step includes the step of extracting the pulses at the equal-azimuth positions of Aoa Center , Aoa Center and Aoa Center ± Aoa Step with the equal-azimuth position of Aoa

[0074] Further, in the third step, after the step of extracting the pulses at the selected equal-azimuth positions, the step includes the step of clearing the number of extracted equal-azimuth position pulses.

[0075] Further, in the fifth step, the PE reflects the uniformity of the re-frequent distribution of the azimuth clustering groups, and the greater the PE, the more disordered the re-frequent distribution, and the smaller the PE, the more uniform the re-frequent distribution.

[0076] Further, M and N are both positive integers.

[0077] Further, in the sixth step, S = 3σ / Aoa Step .

[0078] Embodiment 2

[0079] A computer device includes a processor and a memory, and the memory stores a computer program, when the computer program is loaded and executed by the processor, the method as described in Embodiment 1 is executed.

[0080] Embodiment 3

[0081] A readable storage medium stores a computer program, and the computer program is loaded and executed by a processor, and the method as described in Embodiment 1 is executed.

[0082] The units described in the embodiments of the present application can be implemented in the form of software or hardware, and the described units can also be arranged in a processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0083] According to an aspect of the embodiments of the present application, there is provided a computer program product or computer program, including computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the methods provided in the various optional implementation manners described above.

[0084] As another aspect, the embodiments of the present application also provide a computer readable medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.

[0085] The parts of the present application not involved are the same as or can be realized by the prior art.

[0086] The above technical solution is only one embodiment of the present application. For those skilled in the art, on the basis of the application disclosed application methods and principles, various types of improvements or modifications can be easily made, and are not limited to the methods described in the above embodiments of the present application. Therefore, the above description is only preferred, and is not limited in meaning.

[0087] In addition to the above examples, those skilled in the art can obtain other embodiments by modifying the above disclosure or using related knowledge or technology, the features of each embodiment can be interchanged or replaced, and the modifications and changes made by those skilled in the art do not deviate from the spirit and scope of the present application, and should be within the protection scope of the claims of the present application.

Claims

1. A bearing clustering method based on reoccurrence frequency distribution entropy, characterized in that, comprising the steps of: First step: according to the system direction finding accuracy to determine the azimuth threshold range, supposing the system direction finding accuracy is σ, select the azimuth step value as Aoa Step ; Second step: divide the pulse azimuth into equal parts by Aoa Step the azimuth resolution unit, and count the number of pulses belonging to the corresponding equal parts; Third step: according to the equal division position obtained in the second step, extracting the pulse at the selected equal division position, if the number of the extracted pulses is greater than or equal to the clustering pulse number threshold GrpPulseNum Gate if the number of the extracted pulses is greater than or equal to the clustering pulse number threshold, the group of clustering pulses is considered valid, otherwise, the group of clustering pulses is considered invalid; Step 4: Repeat Step 3 to extract other azimuth cluster group pulses except the selected position until all valid azimuth cluster pulse groups are obtained DoaCluster i , i = 1 ,2,…, M , M is the number of azimuth cluster result groups; (1) In formula (1), P is the total number of pulses. Step 5: The azimuth clustering result of step 4 is subjected to the cluster of repetition frequency to obtain PriCluster ij , i =1,2,…, M , j =1,2,…, N ; PriCluster ij the first i azimuth clustering result is subjected to the cluster of repetition frequency to obtain the first j clustering result, if the clustering PriCluster ij contains the number of pulses K ij , then the corresponding repetition frequency probability P ij is as follows: P ij = K ij / P (2) Repetition frequency distribution entropy PE According to equation (3) it follows that: PE (3) Step 6: Repeat steps 3-5 with Aoa Gate = i × Aoa Step as the orientation threshold, i =1,2,…, S and obtain the corresponding PE i , i =1,2,….. S ; Step 7: Calculate the azimuth threshold corresponding to the minimum PRF entropy AoaGate MinPritropy and cluster the pulses according to the azimuth threshold.

2. The method of claim 1, wherein, In the first step, the azimuth threshold range is determined according to the system direction-finding accuracy, comprising the steps of: Aoa Step The azimuth threshold range is set to (0, 3σ] as the minimum step value.

3. The method of claim 1, wherein, In the second step, the method of Aoa Step The azimuth resolution unit divides the pulse azimuth equally, comprising the step of dividing the pulse azimuth into corresponding equal parts from 0° to 360°.

4. The method of claim 1, wherein, In the third step, the equal division of the orientation according to the second step, the pulse of the selected equal division position is extracted, including the steps of: equal division of the orientation with the equal division position of the maximum pulse number as the center Aoa Center , the pulse of the center equal division position is extracted Aoa Center and Aoa Center ± Aoa Step the equal division position of the pulse.

5. The method of claim 1, wherein, In the third step, after extracting the pulses of the selected aliquot position, comprising the step of: clearing the number of extracted aliquot position pulses.

6. The method of claim 1, wherein, In the fifth step, PE reflects the uniformity of the re-frequent distribution of the clustering grouping in each orientation, if PE the greater, the more chaotic the re-frequent distribution is, PE the smaller, the more uniform the re-frequent distribution is.

7. The method of claim 1, wherein, M, N are both positive integers.

8. The method of claim 2, wherein, In a sixth step, S = 3σ / Aoa Step .

9. A computer device, comprising: The computer device comprises a processor and a memory, and the memory stores a computer program, when the computer program is loaded and executed by the processor, the method as claimed in any one of claims 1-8 is executed.

10. A readable storage medium, characterized by, A computer program is stored in a readable storage medium, and the computer program is loaded and executed by a processor, and the method as claimed in any one of claims 1-8 is executed.

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