Mitigating Peak Sidelobe Levels in Pulse Compression Radar using Artificial Neural Networks

In this paper, Artificial Neural Networks (ANNs) are being considered to obtain low sidelobe pattern for binary codes and thereby to improve the performance of pulse compression radar. Pulse compression is a popular technique used for improving range resolution in the radar systems. This paper proposes a new approach for Pulse Compression using various types of ANN networks like Multi-Layer Perception (MLP), Recursive Neural Networks (RNN), Radial Basis Function (RBF) and Recurrent Radial Basis Function (RRBF) and a special class of Feed-Forward Wavelet Neural Network (WNN) with one input layer, one output layer and one hidden layer are being considered. Networks of 13-bit Barker code and extended binary Barker codes of 35, 55 and 75 length codes were used for the implementation and thereby to improve the performance of pulse compression radar. WNN-based networks using Morlet and Sigmoid activation function in hidden and output layers respectively, a special class of Artificial Neural Network is considered in this paper. The performance metrics used are Peak Sidelobe Ratio (PSLR), Integrated Sidelobe Ratio (ISLR) and Signal-to-Sidelobe Ratio (SSR). Further the performance in terms of range and Doppler resolution is also presented in this paper. Better performance in terms of sidelobe reduction can be achieved with ANNs compared to Autocorrelation Function (ACF) called as matched filter. If the sidelobe values are high there is possibility of masking weaker return signals and there by detection becomes difficult. From this paper it can be established that RRBF gives better result than other ANN networks. Further, WNN gives the best performance even compared with RRBF in terms of sidelobe reduction in pulse compression radar.