Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine
AbstractA novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. View Full-Text
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Zhao, F.; Liu, Y.; Huo, K.; Zhang, S.; Zhang, Z. Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine. Sensors 2018, 18, 173.
Zhao F, Liu Y, Huo K, Zhang S, Zhang Z. Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine. Sensors. 2018; 18(1):173.Chicago/Turabian Style
Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai. 2018. "Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine." Sensors 18, no. 1: 173.