A Bi-SRU Neural Network Based on Soft Attention for HRRP Target Recognition

Abstract

Radar automatic target recognition (RATR) plays a significant role in military applications. The high resolution range profiles (HRRP) contain a large amount of information, such as the distribution of the scatterers, target size and structure, so the HRRP has great prospects in the field of RATR. The traditional HRRP based RATR systems need to extract features manually, which is very difficult and complex to obtain excellent features. To solve this problem, this paper proposed a bidirectional simple recurrent unit network based on soft attention mechanism (SAMBi-SRU) to achieve HRRP target recognition. The simulation results demonstrate that the proposed model can extract robust features from the HRRP data effectively and obtain good performance and noise immunity in HRRP based target recognition.