Abstract
Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.
Highlights
Due to the powerful capability to detect, recognize, and track targets under the conditions of all weather, radar is widely used in many weapon systems, and it has become an indispensable electronic equipment in military [1]
For the proposed CV-convolutional neural networks (CNNs), we first considered the impact of different activation function layers and different batch normalmalization (BN) layers on its recognition effect
13 three of 20 activation functions were utilized to explore the influence of different activation functions on the recognition effect of the complex-valued CNN (CV-CNN)
Summary
Due to the powerful capability to detect, recognize, and track targets under the conditions of all weather, radar is widely used in many weapon systems, and it has become an indispensable electronic equipment in military [1]. Some jamming signal recognition methods based on feature extraction were proposed. In [17], a CNN-based method was proposed for the recognition of radar jamming, which could recognize nine typical radar jamming signal. Based on the CNN, Wu et al [19] proposed an automatic radar jamming signal recognition method, which could recognize five kinds of radar jamming signal. Since the input and model parameters of the RVCNN-based methods are RV, they have insufficient significance for phase and amplitude information and are more suitable for processing RV data. To address the issue that the existing RV-CNN cannot make good use of CV jamming signal, a CV-CNN is proposed to simultaneously extract the features of real and imagery radar signals, which improves the recognition accuracy of a radar jamming signal.
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