Radar HRRP Feature Fusion Recognition Method Based on ConvLSTM Network with Multi-Input Gate Recurrent Unit

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 recogni...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-12, Vol.16 (23), p.4533
Main Authors: Yang, Wei, Chen, Tianqi, Lei, Shiwen, Zhao, Zhiqin, Hu, Haoquan, Hu, Jun
Format: Article
Language:English
Subjects:
Accuracy
Automatic target recognition
convolutional long short-term memory
Datasets
Domains
Feature extraction
feature fusion
Geometrical optics
high-resolution range profile
Information processing
Long short-term memory
Magnetic fields
Methods
Military air strikes
multi-input gate recurrent unit
Radar
Radar systems
ship targets
Simulation
Target acquisition
Target recognition
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16234533