Abstract
Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.
Highlights
One of the most important functions of radar countermeasure systems is that radar emitter signal recognition, in which classification and recognition of intercepted radar signals are carried out to determine the radar type, purpose, carrier, threat level, and recognition credibility of the radar [1]
In a study by Zhao et al, the Margenau–Hill time-frequency distribution and smooth pseudo-Wigner–Ville distribution (SPWVD) were used as signal features, and a classifier was built for radar emitter signal recognition based on an automatic encoder (AE), a deep belief network (DBN), and a convolutional neural network (CNN) [7]
To achieve the above requirements, in this study, a radar emitter signal recognition method based on a 1D deep residual shrinkage network (DRSN) is proposed
Summary
One of the most important functions of radar countermeasure systems is that radar emitter signal recognition, in which classification and recognition of intercepted radar signals are carried out to determine the radar type, purpose, carrier, threat level, and recognition credibility of the radar [1]. In a study by Zhao et al, the Margenau–Hill time-frequency distribution and smooth pseudo-Wigner–Ville distribution (SPWVD) were used as signal features, and a classifier was built for radar emitter signal recognition based on an automatic encoder (AE), a deep belief network (DBN), and a CNN [7]. Based on the deep Q-learning network (DQN) [8], the Cohen’s class time-frequency distributions were used for signal recognition. It is necessary to develop a new radar emitter signal recognition model that has the ability to process different types of noise, but can achieve a high recognition rate under strong noise conditions. To achieve the above requirements, in this study, a radar emitter signal recognition method based on a 1D deep residual shrinkage network (DRSN) is proposed.
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