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

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.

Key words: target detection, low-small-slow target, deep learning, autoencoder, adaptive optimization algorithm