Dual deep neural network-based radar range profile target identification method

Abstract

The invention belongs to the technical field of radars and particularly relates to a dual deep neural network-based radar range profile target identification method. The method comprises the steps of firstly performing preprocessing operations of random distance disturbance, sample expansion, noise addition and the like on range profile data of a target to enhance the robustness of an identification system; secondly in combination with a deep learning theory, proposing a dual deep neural network (DDNN) with an adaptive learning rate, and performing unsupervised pre-training and supervised fine adjustment on the DDNN to obtain DDNN model parameters; thirdly performing pre-identification on test samples by utilizing the DDNN to obtain pre-identification results of the samples in two sub-networks; and finally according to the pre-identification results, performing time-space multi-level decision fusion by utilizing an improved DS evidence theory to obtain a target identification result.

Description

[0001] technical field [0002] The invention belongs to the technical field of radar, and in particular relates to a radar one-dimensional range image target recognition method based on a double deep neural network. Background technique [0003] With the continuous development of deep learning theory, related algorithms based on deep learning have been widely used in many target recognition fields. However, in target recognition based on one-dimensional range images, due to the small number of training samples, the deep model is prone to overfitting problems during the learning process, which ultimately affects the recognition results. Therefore, it is necessary to study a new deep learning model according to the characteristics of one-dimensional image data to further improve the recognition rate. Contents of the invention [0004] The purpose of the present invention is to provide a new dual-depth neural network model with adaptive learning rate for the target recognitio...

AI Technical Summary

Problems solved by technology

However, in target recognition based on one-dimensional range images, due to the small number of training samples, the deep model is prone to overfitting problems during the learning process, which ultimately affects the recognition results.

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