![]() ![]() In the past few years, deeper learning techniques have made great breakthroughs in various pattern recognition tasks, providing inspiration for radar signal recognition. ![]() However, manual feature extraction is time-consuming and requires the assistance of expert knowledge. The literature in and the literature in employ traditional machine learning methods to manually extract features from radar signals or signal time-frequency images. This method achieves 94% recognition accuracy for eight kinds of signals when the signal-to-noise ratio is −2 dB, but shows a low recognition rate for Frank codes and P codes. propose to extract features of radar signals from both time and time-frequency domain, and achieve signal classification using the Elman neural network (ENN) based on the extracted features. Under the condition of SNR = 0 dB, the recognition accuracy reaches above 80%. Next, useful features extracted using image processing techniques are used to realize the recognition of five kinds of radar signals. In, the Choi–Williams distribution (CWD) is used to process radar signals in the time and frequency domain simultaneously. The general process of radar signal recognition includes time-frequency transformation, feature extraction, and classification, as shown in Figure 1. Recently, some scholars attempted to recognize radar signals in the time-frequency domain. Through frequency domain transformation and time-frequency transformation of radar signals, more intra-pulse information of the signals can be reflected. However, the features provided by the time domain signals are limited. In, radar signal classification is achieved through the autocorrelation function and the directed graph model. For example, in, wavelet ridges and high-order statistics are used to extract signal features. Some scholars describe the characteristics of one-dimensional original waveforms through feature calculation. More attention is now paid to the intra-pulse characteristics of the radar signal. However, 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. 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. Low probability of intercept (LPI) radar waveform recognition, as an important and challenging issue in electronic countermeasures, has become a current research focus. In recent years, 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. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. 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. 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. 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. The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. ![]()
0 Comments
Leave a Reply. |