Development of Radar Pulse Compression Techniques Using
Computational Intelligence Tools

Abstract

Pulse compression techniques are used in radar systems to avail the benefits of large range detection capability of long duration pulse and high range resolution capability
of short duration pulse. In these techniques a long duration pulse is used which is either phase or frequency modulated before transmission and the received signal
is passed through a filter to accumulate the energy into a short pulse. Usually, a matched filter is used for pulse compression to achieve high signal-to-noise ratio
(SNR). However, the matched filter output i.e. autocorrelation function (ACF) of a modulated signal is associated with range sidelobes along with the mainlobe.
These sidelobes are unwanted outputs from the pulse compression filter and may mask a weaker target which is present nearer to a stronger target. Hence, these
sidelobes affect the performance of the radar detection system. In this thesis, few investigations have been made to reduce the range sidelobes using computational
intelligence techniques so as to improve the performance of radar detection system.
In phase coded signals a long pulse is divided into a number of sub pulses each of which is assigned with a phase value. The phase assignment should be such that the
ACF of the phase coded signal attain lower sidelobes. A multiobjective evolutionary approach is proposed to assign the phase values in the biphase code so as to achieve
low sidelobes. Basically, for a particular length of code mismatch filter is preferred over matched filter to get better peak to sidelobe ratio (PSR). Recurrent neural
network (RNN) and recurrent radial basis function (RRBF) structures are proposed as mismatch filters to achieve better PSR values under various noise conditions, Doppler shift and multiple target environment.