Radar HRRP Feature Fusion Recognition Method Based on ConvLSTM Network with Multi-Input Gate Recurrent Unit - Wei Yang; Tianqi Chen; Shiwen Lei; Zhiqin Zhao; Haoquan Hu; Jun Hu (Remote Sensing)
Recently, the radar high-resolution range profiles (HRRPs) have gained significant attention in the field of radar automatic target recognition due to their advantages of being easy to acquire, having a small data footprint, and providing rich target structural information. However, existing recognition methods typically focus on single-domain features, utilizing either the raw HRRP sequence or the extracted feature sequence independently. To fully exploit the multi-domain information present in HRRP sequences, this paper proposes a novel target feature fusion recognition approach. By combining a convolutional long short-term memory (ConvLSTM) network with a cascaded gated recurrent unit (GRU) structure, the proposed method leverages multi-domain and temporal information to enhance recognition performance. Furthermore, a multi-input framework based on learnable parameters is designed to improve target representation capabilities. Experimental results of 6 ship targets demonstrate that the fusion recognition method achieves superior accuracy and faster convergence compared to methods relying on single-domain sequences. It is also found that the proposed method consistently outperforms the other previous methods. And the recognition accuracy is up to 93.32% and 82.15% for full polarization under the SNRs of 20 dB and 5 dB, respectively. Therefore, the proposed method consistently outperforms the previous methods overall.