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Radar target detection method

A detection method and radar target technology, applied in neural learning methods, measurement devices, radio wave measurement systems, etc., can solve problems such as low recognition rate and poor robustness

Pending Publication Date: 2021-05-18
CHINA ELECTRONICS TECH GRP CORP NO 14 RES INST
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Problems solved by technology

[0003] In recent years, with the rapid development of deep learning, some experts and scholars have carried out related research on radar target detection and recognition based on deep learning and neural network: radar target recognition method based on deep learning network (patent publication number: CN104459668A) mainly solves In the existing technology, when the radar high-resolution range image is recognized, the recognition rate is low and the robustness is poor, that is, the neural network is used to solve the target recognition problem

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Embodiment 1

[0037] Such as figure 1 As shown, a kind of radar target detection method provided by the present invention comprises the following steps:

[0038] 1. Generate training model

[0039] Target recognition based on deep neural network is a kind of supervised learning. It automatically extracts target features through convolutional network. When the data sample is larger, its generalization ability is stronger. Therefore, before using a model, it is necessary to organize training samples to train it according to the detection requirements. After the training is completed, the model can be deployed for use.

[0040] 1) Model selection

[0041] Use a deep neural network to detect radar targets, you can use a self-designed network, or use a public network. The present invention uses disclosed YoloV3, RetinaNet or CenterNet to carry out the detection of radar target.

[0042] 2) Generate training samples

[0043] The training samples include a plurality of sample pictures, and ea...

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Abstract

The invention provides a radar target detection method, which comprises the steps of generating a training sample: the training sample comprising a plurality of sample pictures, each sample picture being a labeled radar channel amplitude and phase diagram, and the radar channel amplitude and phase diagram being formed by mapping a single-frame radar echo; training the model by using the training sample to generate a training model; processing the radar data to generate a to-be-detected picture; and inputting a to-be-detected picture to the training model, and detecting a target position and a target classification through the training model. According to the method, the training samples can be synchronously and continuously supplemented, new sample training is carried out on the trained model through transfer learning, continuous iterative updating of the model is realized, and thus the target detection capability of the radar can be continuously improved.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and relates to a radar target detection method. Background technique [0002] Target detection technology is one of the most critical technologies in radar signal processing. The performance of target detection has a great influence on the detection power of radar. Traditional radar signal processing mainly includes pulse compression (DPC), moving target indication (MTI), channel correction, beam forming (DBF), coherent or non-coherent accumulation, and constant false alarm processing (CFAR). CFAR detection is a commonly used radar target detection technology at present, mainly including unit average CA CFAR, ordered statistics OS CFAR and ML class CFAR, etc. Its main features are: researchers establish a clutter model, conduct formula derivation based on model assumptions, strive to derive the physical model that is closest to the objective reality, and perform target detection...

Claims

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Application Information

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IPC IPC(8): G01S13/89G06N3/08G06N3/04
CPCG01S13/89G06N3/08G06N3/04
Inventor 王众潘美艳杨予昊于俊朋孙俊
Owner CHINA ELECTRONICS TECH GRP CORP NO 14 RES INST
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