SIDELOBE REDUCTION USING WAVELET NEURAL NETWORK FOR BINARY CODED PULSE COMPRESSION

Abstract

Pulse compression technique is a popular technique used for improving waveform in radar systems. Series of undesirable sidelobes usually accompany the technique that may mask small targets or create false targets. This paper proposed a new approach for pulse compression using Feed-forward Wavelet Neural Network (WNN) with one input layer, one output layer and one hidden layer that consists of three neurons. Networks of 13-bit Barker code and 69-bit Barker code were used for the implementation. WNN-based back-propagation (BP) learning algorithm was used in training the networks. These networks used Morlet and sigmoid activation functions in hidden and output layer respectively. The simulation results from the proposed method shows better performance in sidelobe reduction where more than 100 dB output peak sidelobe level (PSL) is achieved, compared to autocorrelation function (ACF). Furthermore, the results show that WNN approach has significant improvement in noise reduction performance and Doppler shift performance compared to Recurrent Neural Network (RNN) and Multi-Layer Perceptron (MLP).