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Unsupervised radar signal sorting method based on clustering and sofm

A radar signal sorting and radar signal technology, which is applied in neural learning methods, instruments, biological neural network models, etc., can solve the problem that radar signals with overlapping parameters cannot be sorted, the sorting accuracy has a great influence, and it is easy to fall into the local maximum. Excellent problems, to achieve the effect of improving the sorting accuracy, improving the sorting accuracy, and improving the processing effect

Active Publication Date: 2022-03-04
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that when the traditional K-means clustering and SOFM neural network are used for radar signal sorting, the preset parameters have a great influence on the correct rate of sorting, and it is easy to fall into local optimum, the network structure is fixed, and it is impossible to sort The technical status of overlapping radar signals with parameters, and an unsupervised radar signal sorting method based on clustering and SOFM

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  • Unsupervised radar signal sorting method based on clustering and sofm
  • Unsupervised radar signal sorting method based on clustering and sofm
  • Unsupervised radar signal sorting method based on clustering and sofm

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Embodiment 1

[0072] This embodiment sets forth the specific implementation of the unsupervised radar signal sorting method based on clustering and SOFM in the present invention when sorting radar signals with overlapping parameters. The implementation flow chart of the present invention is as follows figure 1 shown.

[0073] The detailed background of the radar simulation data is as follows:

[0074] The characteristic parameters of the radar signal, that is, the pulse description word PDW is composed of six parameters: the pulse angle of arrival DOA, the pulse frequency RF, the pulse amplitude PA, the pulse width PW and the pulse time of arrival TOA. This example adopts the three characteristic parameters DOA, RF and PW. Radar signal sorting, that is, the value of parameter n in step 2.2 is 3;

[0075] In this example, PDW data software is used to simulate 8 radars with overlapping parameters. Each radar has a parameter that is equal or similar to the corresponding parameters of other ra...

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Abstract

The invention relates to an unsupervised radar signal sorting method based on clustering and SOFM, and belongs to the technical field of deep learning and radar signal sorting. The radar signal sorting method combines density dynamic clustering with a SOFM neural network capable of automatic structural adjustment, and constructs an unsupervised radar signal sorting that combines density clustering and SOFM without preset parameters; specifically: first , carry out density dynamic clustering on the radar signal for pre-sorting processing, and obtain the relevant parameters required in the main sorting stage; secondly, use the relevant parameters obtained in the pre-sorting to construct a SOFM network that can automatically realize structural adjustment and do the main sorting of radar signals processing to obtain the final sorting result. The radar signal sorting method does not need to preset initial values, realizes unsupervised sorting of radar signals, improves the accuracy of radar signal sorting, and has a better processing effect on radar signals with overlapping parameters.

Description

technical field [0001] The invention relates to an unsupervised radar signal sorting method based on clustering and SOFM, and belongs to the technical field of deep learning and radar signal sorting. Background technique [0002] Radar signal sorting is a key technology in electronic warfare. The environment faced by the modern radar reconnaissance system is becoming more and more complex, the density of radar signals is increasing, and the parameter overlap is becoming more and more serious. The real-time signal processing of the radar reconnaissance system is facing severe challenges. K-means clustering has the advantages of simplicity, speed, and good practicability. SOFM neural network has the characteristics of self-organization learning and is suitable for high-dimensional data clustering. They are all commonly used methods for radar signal sorting. [0003] Although these two methods are classic signal sorting methods, they both have certain shortcomings: (1) The tra...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06F2218/00G06F18/23213
Inventor 傅雄军苏顺启蒋文尹先晗杨婧芳丛培羽赵聪霞
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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