Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Abstract

Low Probability of Intercept (LPI) radars are developed on an advanced architecture by making use of coded waveforms. Detection and classification of radar waveforms are important in many critical applications like electronic warfare, threat to radar and surveillance. Precise estimation of parameter and classification of the type of waveform will provide information about the threat to the radar and also helps to develop sophisticated intercept receiver. The present work is on classification of modulation waveforms of LPI radar using multilayer perceptron neural (MLPN) network. The classification approach is based on the following two steps. In the first step, the waveforms are analysed using cyclstationary technique which models the signal in bi-frequency (BF) plane. Using this algorithm, the BF images of the signals are obtained. In the second step, the BF images are fed to a feature extraction unit to get the salient features of the waveform and then to the multilayer perceptron neural (MLPN) network for classification. Nine types of noise free modulation waveforms (Frank, four polyphase codes and four poly time codes) are classified using the images obtained in the first step. The success rate achieved is 100 % for noise free signals. The experiment is repeated for various noise levels up to -12dB SNR. The noisy signals, before feeding to the MLPN network, are denoised using two types of denoising filters connected in cascade and the classification success rate achieved is 93.3% for signals up to -12dB SNR.