Analysis and classification of airborne radar signal types using time-frequency analysis

Abstract

An electronic warfare support measure system is used by the military for intelligence gathering, threat detection, and as a support to electronic attack system. Its main feature is to determine the frequency parameters and pulse characteristics of the received radar signal. The estimated signal parameters are then used as input to a classifier network to determine the identity of the received signal. This project describes airborne radar type analysis and classification (ARTAC) system that uses the spectrogram to obtain the time-frequency representation (TFR) and apply analysis tools such as the instantaneous energy, instantaneous frequency and other related tools to estimate various signal parameters. The estimated parameters are used as input to the rule-based classifier which classifies the signal appropriately. Monte-Carlo simulation is then carried out to estimate the accuracy of signal classification at various signal-to-noise ratios (SNRs) in additive white Gaussian noise (AWGN). The proposed system achieves 90 percent classification accuracy at minimum SNR of 6.2dB