Article
Recognition of Noisy Radar Emitter Signals Using a
One-Dimensional Deep Residual Shrinkage Network
Shengli Zhang , Jifei Pan *, Zhenzhong Han and Linqing Guo
Citation: Zhang, S.; Pan, J.; Han, Z.;
Guo, L. Recognition of Noisy Radar
Emitter Signals Using a
One-Dimensional Deep Residual
Shrinkage Network. Sensors 2021, 21,
7973. https://doi.org/10.3390/
s21237973
Academic Editors: YangQuan Chen,
Subhas Mukhopadhyay, Nunzio
Cennamo, M. Jamal Deen,
Junseop Lee and Simone Morais
Received: 14 October 2021
Accepted: 26 November 2021
Published: 29 November 2021
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4.0/).
Electronic Countermeasure Institute, National University of Defense Technology, Hefei 230037, China;
zhangshli@mail2.sysu.edu.cn (S.Z.); hzz@nudu.edu.cn (Z.H.); guolq@nudt.edu.cn (L.G.)
* Correspondence: panjifei17@nudt.edu.cn; Tel.: +86-153-2449-6223
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.
Keywords:
radar emitter signal recognition; high noise; one-dimensional residual shrinkage network;
soft thresholding
1. Introduction
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
]. Therefore, accurate radar emitter signal recognition
is essential for subsequent radar analysis and action preparation.
The existing radar emitter signal recognition methods can be divided into two cat-
egories. The first category includes traditional signal analysis methods, including gray
correlation analysis [
2
], template matching [
3
], fuzzy matching [
4
], and attribute mea-
surement [
5
]. However, there are various deficiencies in the traditional methods, the
recognition performance is dependent on the richness of prior knowledge, tolerance rate
and robustness are poor, and they do not have automatic learning abilities. The second
category includes deep learning methods, which are usually based on time-frequency
transform. For example, the classification and recognition of signals were achieved us-
ing the Choi–Williams time-frequency distribution with convolutional neural network
(CNN) [
6
]. 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 then
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.
Wu et al. [
9
] used one-dimensional (1D) CNN for radar signal recognition. However, the
parameter optimization of traditional deep learning methods is a difficult task, the error
function gradient may gradually become inaccurate in the process of reverse propagation.
When the network layer is too deep, the parameters in the initial layers cannot be optimized
Sensors 2021, 21, 7973. https://doi.org/10.3390/s21237973 https://www.mdpi.com/journal/sensors
