A Technique for Pulse RADAR Detection Using RRBF Neural Network

Abstract

Pulse compression technique combines the high energy characteristic of a longer pulse width with the high resolution characteristic of a narrower pulse width. The major aspects that are considered for a pulse compression technique are signal to sidelobe ratio (SSR), noise and Doppler shift performances. The traditional algorithms like autocorrelation function (ACF), recursive least square (RLS) algorithm, multilayer perceptron (MLP), radial basis function (RBF) and recurrent neural network (RNN) have been applied for pulse compression and their performances have also been studied. This paper presents a new approach for pulse compression using recurrent radial Basis function (RRBF) neural network. 13 and 35-bit Barker codes are taken as input to RRBF network for pulse compression and the results are compared with MLP, RNN and RBF network based pulse compression schemes. The analysis of simulation results reveals that RRBF yields higher SSR, improved noise performance, better Doppler tolerance and hence more robust for pulse radar detection compared to the other techniques.