Radar signal recognition exploiting information geometry and support vector machine

Abstract

Abstract Aiming at the recognition of low‐probability‐of‐intercept (LPI) radar signals, a support vector machine (SVM)‐based algorithm is proposed, where the information geometry theory is utilised to optimise the kernel function of the SVM. Since signals with different modulations have different characteristics in the time‐frequency domain, the time‐frequency transformation result of the LPI radar signal is considered as an image, referred to as the time‐frequency image, and computer vision techniques are utilized to perform recognition. Specifically, the time‐frequency images of different LPI radar signals are obtained via the Choi‐Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time‐frequency features. Then, an SVM is adopted to recognise LPI radar signals due to its superiority in addressing the dimension disaster and non‐linear inseparability issue. The extracted time‐frequency features are fed into the SVM for classification and recognition. Note that the classification performance of SVM depends on the kernel function. Therefore, in the proposed algorithm, information geometry theory is exploited to improve the Gaussian kernel function, and the maximum margin between different categories of samples is further enlarged. As a consequence, the recognition accuracy for LPI radar signals with similar time‐frequency images is effectively improved. In addition, the proposed algorithm has better robustness to small samples than other deep learning‐based algorithms, since the SVM method minimises the structural risk instead of the empirical risk. Simulation results verify the effectiveness of the proposed algorithm.

Keywords

• information geometry
• LPI radar signal
• signal modulation identification
• support vector machine