Symmetry Feature Estimation Over STFT Transform to Improve Radar Classification Based on the Wisard Weightless Neural Network

Automated early recognition of enemy radar emissions are essential for the survival of a warship. Radar signals intercepted by a passive digital Electronic Support Measure (ESM) receiver can be classified based on the type of intrapulse modulation. The modulation classification is typically based on features extracted from the preprocessed radar’s signal. Low Probability of Intercept (LPI) radars can use phase or frequency modulated signals to make radar emissions difficult for the enemy to detect. This paper proposes the use of a new feature, the symmetry measured in a Time-Frequency (TF) matrix, to improve intrapulse radar modulation classification. The analysis of the symmetry in a STFT matrix is characterized by an image processing problem, where the matrix is interpreted as a grayscale image. This paper also proposes the use of Weightless Neural Network WiSARD in identifying symmetry patterns in the Short Time Fourier Transform (STFT) matrix, a characteristic present in signals with Barker and Polytime phase modulations and which can be used by classifiers to discriminate them from signals with other types of modulations such as polyphase modulations (P1, P2, P3, P4 and Frank), linear frequency modulation (LFM), and Non Linear Frequency Modulation.