Radar High-Resolution Range Profile Ship Recognition Using Two-Channel Convolutional Neural Networks Concatenated with Bidirectional Long Short-Term Memory

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-04, Vol.13 (7), p.1259
• Main Authors: Lin, Chih-Lung, Chen, Tsung-Pin, Fan, Kuo-Chin, Cheng, Hsu-Yung, Chuang, Chi-Hung
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Automatic target recognition; bidirectional long short-term memory network (BiLSTM); convolutional neural networks (CNN); Deep learning; Discriminant analysis; Feature extraction; graphics processing unit (GPU); High resolution; high-resolution range profile (HRRP); Long short-term memory; long short-term memory network (LSTM); Machine learning; Neural networks; Radar; radar automatic target recognition (RATR); Recurrent neural networks; Reflected waves; Remote sensing; Signal processing; Target recognition
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13071259

Radar automatic target recognition is a critical research topic in radar signal processing. Radar high-resolution range profiles (HRRPs) describe the radar characteristics of a target, that is, the characteristics of the target that is reflected by the microwave emitted by the radar are implicit in it. In conventional radar HRRP target recognition methods, prior knowledge of the radar is necessary for target recognition. The application of deep-learning methods in HRRPs began in recent years, and most of them are convolutional neural network (CNN) and its variants, and recurrent neural network (RNN) and the combination of RNN and CNN are relatively rarely used. The continuous pulses emitted by the radar hit the ship target, and the received HRRPs of the reflected wave seem to provide the geometric characteristics of the ship target structure. When the radar pulses are transmitted to the ship, different positions on the ship have different structures, so each range cell of the echo reflected in the HRRP will be different, and adjacent structures should also have continuous relational characteristics. This inspired the authors to propose a model to concatenate the features extracted by the two-channel CNN with bidirectional long short-term memory (BiLSTM). Various filters are used in two-channel CNN to extract deep features and fed into the following BiLSTM. The BiLSTM model can effectively capture long-distance dependence, because BiLSTM can be trained to retain critical information and achieve two-way timing dependence. Therefore, the two-way spatial relationship between adjacent range cells can be used to obtain excellent recognition performance. The experimental results revealed that the proposed method is robust and effective for ship recognition.