Affiliation:
1. National Digital Switching System Engineering and Technical Research Centre, Zhengzhou, China
Abstract
Using traditional neural network algorithms to adapt to high-resolution range profile (HRRP) target recognition is a complex problem in the current radar target recognition field. Under the premise of in-depth analysis of the long short-term memory (LSTM) network structure and algorithm, this study uses an attention model to extract data from the sequence. We build a dual parallel sequence network model for rapid classification and recognition and to effectively improve the initial LSTM network structure while reducing network layers. Through demonstration by designing control experiments, the target recognition performance of HRRP is demonstrated. The experimental results show that the bidirectional long short-term memory (BiLSTM) algorithm has obvious advantages over the template matching method and initial LSTM networks. The improved BiLSTM algorithm proposed in this study has significantly improved the radar HRRP target recognition accuracy, which enhanced the effectiveness of the improved algorithm.
Funder
Research Team Development fund
Subject
Electrical and Electronic Engineering
Reference29 articles.
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2. Radar automatic target recognition based on complex high-resolution range profiles;L. Du
3. Frequency-Hopping Code Design for MIMO Radar Estimation Using Sparse Modeling
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