An angle pre-sorting method for dynamic clustering in DSP

Abstract

The invention discloses an angle pre-sorting method for dynamic clustering of DSP, which belongs to the field of electronic monitoring. First, the received intermediate frequency signal is subjected to signal detection, parameter measurement, phase correction, and defuzzification angle measurement, and full pulse data with azimuth and elevation information is output. Then, the azimuth and elevation of the first full pulse input are used as the initial angle clustering library, and then the remaining full pulses are input, and the azimuth and elevation of each input full pulse are subjected to two-dimensional dynamic clustering. When all full pulses are clustered, the azimuth and elevation in the angle clustering library are averaged to obtain the final angle clustering library, and the angle pre-sorting is completed.

Description

Technical Field

[0001] The invention belongs to the field of electronic monitoring, and in particular relates to an angle pre-sorting method for dynamic clustering of DSP. Background Art

[0002] In modern electronic warfare, the electromagnetic environment in which electronic monitoring equipment works is complex and changeable. In order to achieve real-time monitoring of the entire scene and wide frequency band, the electronic monitoring receiver needs to have a large instantaneous bandwidth and high sensitivity. At this time, the receiver can detect a large number of pulse responses, most of which come from interference signals in different directions in the monitoring electromagnetic environment. The existence of these interference signals with different angles increases the angle pre-sorting time overhead of signal sorting. In the case of limited DSP processing resources, the time of main signal sorting is greatly shortened. In severe cases, it may lead to the loss of key radiation source detection results, which seriously affects the adaptability of electronic monitoring equipment to complex electromagnetic environments. In this regard, based on the commonly used electronic monitoring receiver structure, the study of effective angle pre-sorting methods can effectively improve the resource utilization of DSP used for signal sorting, which is of great significance for improving the detection performance of electronic monitoring equipment. Summary of the invention

[0003] The present invention proposes an angle pre-sorting method for dynamic clustering of DSP, which realizes fast angle pre-sorting in complex electromagnetic environment. Compared with the commonly used clustering algorithm, it greatly reduces the amount of calculation and the required storage resources, effectively reduces the influence of interference pulses, and improves the signal sorting and recognition capabilities of electronic monitoring equipment.

[0004] The technical solution for implementing the present invention is: a method for pre-sorting angles of dynamic clustering for DSP, characterized by comprising the following steps:

[0005] Step 1: Perform AD sampling and multi-phase filtering on the received intermediate frequency signal, and then perform signal detection to obtain a full detection pulse, and then proceed to step 2.

[0006] Step 2: Perform parameter measurement, phase detection, phase correction and deambiguation angle measurement on the detected full pulse in sequence to obtain full pulse data with azimuth and elevation angle information, and then proceed to step 3.

[0007] Step 3: Perform two-dimensional dynamic clustering of the azimuth and elevation angles of each full pulse in the full pulse data with azimuth and elevation angle information, and use the azimuth and elevation angles of the first full pulse input as the initial angle clustering library.

[0008] When all full pulses are clustered, the azimuth angles and elevation angles in the angle cluster library are averaged to obtain the final angle cluster library.

[0009] For all full pulses, the Euclidean distance is calculated according to the azimuth and elevation cluster center values ​​of the final angle cluster library. The full pulse is marked with the library number to obtain the final pulse angle clustering result, otherwise the final angle clustering library is updated, where is the standard deviation of the angle measurement error.

[0010] Compared with the prior art, the present invention has the following significant advantages: compared with the traditional clustering algorithm, the present invention adopts a dynamic clustering method, which greatly reduces the amount of calculation and storage space, and is convenient for implementation on embedded chips such as DSP. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of the angle pre-sorting method for dynamic clustering of DSP of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] The technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0014] The following will further introduce the specific implementation method, as well as the technical difficulties and inventive points of this invention in combination with this design example.

[0015] Combination Figure 1 The angle pre-sorting method for dynamic clustering of DSP described in the present invention comprises the following steps:

[0016] Step 1: After AD sampling and multi-phase filtering of the received intermediate frequency signal, signal detection is performed to obtain a full detection pulse, and then proceed to step 2.

[0017] Step 2: Perform parameter measurement, phase detection, phase correction and deambiguation angle measurement on the detected full pulse in sequence to obtain full pulse data with azimuth and elevation angle information, and then proceed to step 3.

[0018] Step 3, performing two-dimensional dynamic clustering of azimuth and elevation angles on each full pulse in the detected full pulse data;

[0019] After all full pulses are clustered, the azimuth angles and elevation angles in the angle cluster library are averaged to obtain the final angle cluster library.

[0020] For all full pulses, the Euclidean distance is calculated according to the azimuth and elevation cluster center values ​​of the final angle cluster library. The full pulse of the is the standard deviation of the angle measurement error, and the final pulse angle clustering result is obtained, otherwise the angle clustering library is updated.

[0021] The specific steps include:

[0022] Step 31, using the azimuth angle and elevation angle of the first full pulse input as the initial angle clustering library;

[0023] Step 32: Input the remaining full pulses, and perform two-dimensional dynamic clustering of the azimuth and elevation angles for each full pulse input, that is, calculate the Euclidean distance between the azimuth and elevation angles in each full pulse and the azimuth and elevation angles in the angle clustering library. , the specific calculation expression is:

[0024] ,

[0025] in, is the azimuth of the current full pulse, is the pitch angle of the current full pulse, is the first in the current angle clustering library Azimuth, is the first in the current angle clustering library Pitch angle, , Indicates the total number of pulses. , Indicates the total number of clusters in the current angle cluster library, that is, the total number of angles in the current angle cluster library.

[0026] Step 33: traverse and calculate the Euclidean distance between all pulses and each cluster center value , if the distance to any angle of the angle clustering library is less than When , the matching process is exited, the angle clustering library is updated, the number of elements in the library is increased by 1, and the azimuth and elevation angle statistics in the angle clustering library are accumulated:

[0027] ,

[0028] in, is the azimuth of the current full pulse, is the pitch angle of the current full pulse, Represents the first Angles, Indicates the total number of pulses under the clustering result, represents the sum of all pulse azimuths under this clustering result, represents the sum of all full pulse pitch angles under this clustering result, Indicates the azimuth cluster center value under the clustering result. Indicates the cluster center value of the pitch angle under the clustering result.

[0029] Step 34: After traversing all the angles in the angle cluster library, there is no angle less than , then add a new angle , as shown below:

[0030] ,

[0031] in, is the azimuth of the current full pulse, is the pitch angle of the current full pulse.

[0032] Step 35: When the total number of added angles is equal to 512, Angle Sort by number, retain the first 256 clustering results, clear the last 256 clustering results, repeat step 32, input the next full pulse information, until all full pulses have completed the clustering operation.

[0033] Step 36: After all full pulses are clustered, average the azimuth and elevation angles in the angle cluster library to obtain the final angle cluster library. .

[0034] in,

[0035] ,

[0036] in, The final angle clustering library Angles.

[0037] Step 37: Calculate the Euclidean distance based on the azimuth and elevation cluster center values ​​in the final angle cluster library. The full pulse is marked with the library number to obtain the final pulse angle clustering result.

[0038] The running time comparison between the present invention and the traditional angle clustering algorithm on DSP is shown in Table 1:

[0039] Table 1 Comparison of running time of the proposed method and the traditional angle clustering algorithm on DSP

[0040] Total pulse number Time used by the present invention Running time of traditional angle clustering algorithm 20000 24.3ms 28.7ms 50000 65.6ms 85.3ms 200000 255.8ms 323.4ms

[0041] Combined with Table 1, as the number of full pulses increases, the processing time saved is more compared with the traditional angle clustering algorithm.

Claims

1. An angle pre-sorting method for dynamic clustering of DSP, characterized in that: The following steps are involved: Step 1: After AD sampling and multi-phase filtering of the received intermediate frequency signal, signal detection is performed to obtain a full detection pulse, and then proceed to step 2; Step 2: perform parameter measurement, phase detection, phase correction and deambiguation angle measurement on the detected full pulse in sequence to obtain full pulse data with azimuth and elevation angle information, and then proceed to step 3; Step 3: Perform two-dimensional dynamic clustering of the azimuth and elevation angles of each full pulse in the full pulse data with azimuth and elevation angle information, and use the azimuth and elevation angle of the first full pulse input as the initial angle clustering library; When all full pulses are clustered, the azimuth angle and elevation angle in the angle cluster library are averaged to obtain the final angle cluster library; For all full pulses, the Euclidean distance is calculated according to the azimuth and elevation cluster center values ​​of the final angle cluster library. For full pulses with a distance less than 3σ, the full pulse is marked with the library number to obtain the final pulse angle clustering result. Otherwise, the final angle cluster library is updated, where σ is the standard deviation of the angle measurement error. In step 3, the following steps are specifically included: Step 31, using the azimuth angle and elevation angle of the first full pulse input as the initial angle clustering library; Step 32: Input the remaining full pulses, and perform two-dimensional dynamic clustering of the azimuth and elevation angles for each full pulse input, that is, calculate the Euclidean distance value D between the azimuth and elevation angles in each full pulse and the azimuth and elevation angles in the angle clustering library, respectively. ij ; Step 33: traverse and calculate the Euclidean distance D between all pulses and each cluster center value ij , if the distance to any angle in the angle cluster library is less than 3σ, the matching process is exited, the angle cluster library is updated, the number of elements in the library is increased by 1, and the azimuth and elevation angle statistics in the angle cluster library are accumulated; Step 34: When all angles in the angle cluster library are traversed and none of them is less than σ, a new angle is added; Step 35: when the total number of newly added angles is equal to 512, sort the M angles by the number C, retain the first 256 clustering results, clear the last 256 clustering results, repeat step 32, input the next full pulse information, until all full pulses have completed the clustering operation; Step 36: After all full pulses are clustered, the azimuth angles and elevation angles in the angle cluster library are averaged to obtain a final angle cluster library; Step 37, calculating the Euclidean distance according to the azimuth angle and elevation angle cluster center values ​​in the final angle cluster library, marking the full pulse with a distance less than 3σ with the library number, and then obtaining the final pulse angle clustering result; In step 32, the remaining full pulses are input, and two-dimensional dynamic clustering of azimuth and elevation angles is performed on each input full pulse, that is, the Euclidean distance value D between the azimuth and elevation angles in each full pulse and the azimuth and elevation angles in the angle clustering library is calculated. ij , the specific calculation expression is: Among them, x i is the azimuth of the current full pulse, y i is the pitch angle of the current full pulse, x j is the jth azimuth in the current angle cluster library, y j is the jth pitch angle in the current angle cluster library, i=1,...,N, N represents the total number of all pulses, j=1,...,M, M represents the total number of clusters in the current angle cluster library, that is, the total number of angles in the current angle cluster library; In step 33, the Euclidean distance D between the whole pulse and each cluster center value is calculated. ij , if the distance to any angle in the angle cluster library is less than 3σ, the matching process is exited, the angle cluster library is updated, the number of elements in the library is increased by 1, and the azimuth and elevation angle statistics in the angle cluster library are accumulated: Among them, x i is the azimuth of the current full pulse, y i is the pitch angle of the current full pulse, E j represents the jth angle of the angle cluster library, C represents the number of full pulses under the clustering result, S1 represents the sum of all full pulse azimuth angles under the clustering result, S2 represents the sum of all full pulse elevation angles under the clustering result, A1 represents the cluster center value of the azimuth angle under the clustering result, and A2 represents the cluster center value of the elevation angle under the clustering result; In step 34, when all angles in the angle cluster library are traversed and none of them is less than σ, a new angle E is added. j+1 , as shown below: Among them, x i is the azimuth of the current full pulse, y i is the pitch angle of the current full pulse; In step 36, after all full pulses are clustered, the azimuth angles and elevation angles in the angle cluster library are averaged to obtain the final angle cluster library, as follows: The final angle clustering library expression is as follows: E′={E1′,E2′,...E j ′...,E′ M }, Among them, E j ′ represents the jth angle of the final angle clustering library.

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