Abstract
The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB.
Highlights
IntroductionAs electromagnetic signals have become increasingly diversified in time domain, frequency domain, spatial distribution, and modulation patterns, the electronic countermeasure environment has become increasingly complex [1]
To solve the problem of the difficulty of feature extraction under low SNR and the To solve the problem of the difficulty of feature extraction under low and the low recognition rate of various types of signals, in this paper, we propose a new approach low recognition rate of various types of signals, in this paper, we propose a new approach for Low probability of intercept (LPI) radar signal recognition based on an asymmetric dual-channel convolutional neural network (CNN) and feature for LPISpecifically, radar signalwe recognition based asymmetricLPI
In order to achieve the recognition of these 12 radar signals under a low signal-to-noise ratio, we propose a new approach based on two-channel CNN feature fusion
Summary
As electromagnetic signals have become increasingly diversified in time domain, frequency domain, spatial distribution, and modulation patterns, the electronic countermeasure environment has become increasingly complex [1]. Low probability of intercept (LPI) radar waveform recognition, as an important and challenging issue in electronic countermeasures, has become a current research focus [2]. In the early days, when the electromagnetic environment was simple, the pulse description word (PDW) was mainly used to realize the sorting and recognition of radar pulse signals [3–6]. With the increasing complexity of the electromagnetic environment, the pulse description word with a single feature can no longer meet the identification requirements for the LPI radar signal with large time width and strong interference [7–9]. More attention is paid to the intra-pulse characteristics of the radar signal. In [10], wavelet ridges and high-order statistics are used to extract signal features
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call