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雷达目标检测深层自编码器自适应优化算法

HOU Xuan CHEN Tao WANG Weiliang

计算机工程与应用2019，Vol.55Issue(13)：165-171,7.

计算机工程与应用2019，Vol.55Issue(13)：165-171,7.DOI:10.3778/j.issn.1002-8331.1805-0269

雷达目标检测深层自编码器自适应优化算法

Deep Atuoecoder Adaptive Learning Algorithm on Radar Target Detection

HOU Xuan ¹CHEN Tao ²WANG Weiliang³

作者信息

1. School of Journalism and Communication, Northwest University of Politics and Law, Xi’an 710122, China 2.College of Aeronautics Engineering, Air Force Engineering University, Xi’an 710038, China 3.College of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
3. College of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
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摘要

Abstract

The difficulties and methods of radar low-small-slow target detection technology are studied. It analyzes the basic model and algorithm of deep autoencoder. By introducing adaptive learning theory, Rumelhart function-Deep Atuoecoder Adaptive learning Algorithm(RDAAA)is proposed, and the convergence of the algorithm is proved. The optimization algorithm avoids the phenomenon of excessive punishment in the network training process, overcomes the disadvantages of excessively high learning rate leading to oscillating and divergent in network or low learning rate leading to reduce network convergence speed. Two types of data sets are used to analyze the pattern recognition ability of RDAAA, Cross-entropy function-Deep Autoencoder learning Algorithm(CDAA)and error Back Propagation Algorithm (BPA). In the case of determining the limit error and selecting the optimal learning rate, the results show that RDAAA has the fastest convergence rate and higher correct recognition rate than CDAA and BPA. Focusing on radar target detection and deep learning theory, the characteristics of low-small-slow target are analyzed, and the target detection problem is transformed into a problem of pattern classification. Using the above three algorithms for target detection simulation experiments, the results show that the performance of RDAAA and CDAA is significantly better than that of BPA, and the detection rate of RDAAA is higher, especially in the low signal-to-noise ratio stage, and the high probability of discovery can still be maintained.

关键词

目标检测/低小慢目标/深度学习/自动编码器/自适应优化算法

Key words

target detection/ low-small-slow target/ deep learning/ autoencoder/ adaptive optimization algorithm

分类

信息技术与安全科学

引用本文复制引用

HOU Xuan,CHEN Tao,WANG Weiliang..雷达目标检测深层自编码器自适应优化算法[J].计算机工程与应用,2019,55(13):165-171,7.

基金项目

国家自然科学基金(No.51507186) （No.51507186）

国家自然科学青年科学基金(No.51509257). （No.51509257）

计算机工程与应用

OA北大核心CSCDCSTPCD

ISSN：1002-8331

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