A radar clutter identification method based on a convolutional neural network

Abstract

The invention relates to a radar clutter identification method based on a convolutional neural network. The method comprises the steps of obtaining clutter data; Dividing the clutter data into training data and test data; Constructing a convolutional neural network; Using the training data to train the convolutional neural network; Testing the trained convolutional neural network by using the testdata; And when the recognition accuracy of the convolutional neural network is greater than a preset accuracy, obtaining an optimal convolutional neural network. According to the method provided by the invention, the clutter data is processed and input into the convolutional neural network, and implicit feature extraction is carried out on the clutter data by utilizing the convolutional neural network, so that the feature extraction efficiency is improved; By reducing the number of clutter data, the sampling cost is reduced, and meanwhile, the recognition efficiency is improved; The convolutional neural network is used to classify and identify the clutter data, the identification accuracy rate reaches 99.51%, and the identification accuracy rate is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a radar clutter recognition method based on a convolutional neural network. Background technique [0002] As we all know, the basic principle of radar is to radiate electromagnetic energy and detect the reflected echo of the target, and then process the echo to different degrees to extract useful information features. The most basic and important function in radar signal processing is object detection. Target detection, as the name implies, is to judge whether the target exists in the echo. Under normal circumstances, the echo will be accompanied by thermal noise generated by the radar receiver, clutter signals, and even jamming signals deliberately released by the enemy. This requires that the radar receiver must have the ability to correctly distinguish the target and distinguish the target information from clutter, noise and interference. [0003...

AI Technical Summary

Problems solved by technology

[0004] However, the disadvantages are that the above-mentioned methods require explicit feature extraction, which is time-consuming and labor-intensive, and the feature extraction efficiency is low; when classifying and identifying the extracted features, more input samples need to be input, and the sampling cost is high. Less efficient and less accurate

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