RADAR Signal Processing Using Multi-Objective Optimization Techniques

Abstract

Pulse compression is used in radar system to achieve the range resolution of short duration pulses and Signal to Noise Ratio (SNR) of long duration pulses. Linear Frequency Modulated (LFM) pulse is one type of signal used in radar. In wide-band radar, for good range resolution, very wide bandwidth is used. The conventional hardware may not be able to sustain this large bandwidth. So the wideband signal is split into narrowband signals. In narrowband signals, frequency changes linearly for complete duration of the pulse. We change the center frequency of each LFM pulse by introducing a frequency step between consecutive pulses. The resultant signal is known as Stepped Frequency Pulse Train (SFPT). When the product of pulse duration and frequency step become more than one, the Autocorrelation Function (ACF) of SFPT yields undesirable peaks, known as grating lobes. Along with grating lobe, the higher peak side lobe can hides the small. Also the wide main lobe width deteriorate the range resolution capability of the signal. Many analytic techniques have been proposed in the literature to select the SFPT parameter to suppress the grating lobe, without paying much attention to side lobe and main lobe width. In this work we compare three MOO algorithms to find the optimized parameter of SFPT. The problem is studied in two ways: In first we take objective of minimization of grating lobes and peak side lobe level. The constraint is of increase in bandwidth. In second problem, our aim is to minimize the main lobe width, which improves the resolution. The objective functions for the second problem are minimization of main lobe width and peak side lobe level. We don’t want high grating lobe amplitude, so we add a constraint, which restrict the maximum grating lobe amplitude below a threshold value. Simulations are carried out for different range of parameter values and the simulation result shows the potential of the MOO approach.