Optimising Sidelobes and Grating Lobes in Frequency Modulated Pulse Compression

Pulse compression is a signal processing technique used in radar systems to achieve long range target detection capability, which is a characteristic of long duration pulse, without compromising the high range resolution capability, which is characteristic of a short duration pulse. For this, the received signal at the receiver is compressed by a matched filter to produce a compressed version of the signal for better resolution. As the range resolution is inversely proportional to the bandwidth, high range resolution is ensured by using a transmitted pulse of greater bandwidth. LFM pulse is better used than a constant frequency pulse because of its larger bandwidth. The bandwidth of a signal can further be increased by taking a train of pulses with the center frequency of consecutive pulses stepped by some frequency step ∆f. A train of pulses with each pulse of duration T, separated by time Tr gives rise to grating lobes in its autocorrelation function (ACF), when T∆f>1. ACF of a single LFM pulse has also sidelobes of its own. Grating lobes and sidelobes may act individually or together to mask smaller targets in close vicinity of a larger target, hence are needed to be reduced. In the first part of the work, two optimization algorithms called Clonal Particle Swarm Optimization and Differential Evolution has been used to find out specific windows that shape an LFM pulse to reduce the ACF sidelobes to their optimal minima. Temporal windows has been found out using three coefficient window expressions and four coefficient window expressions. Resulting windows have been found to reduce sidelobes to an extent which was not possible by the classical windows. Grating lobes in a train of pulses can be lowered by the use of LFM pulses instead of fixed frequency pulses. Nullification of the ACF grating lobes is possible when T, ∆f, and B satisfy a special relationship that puts the ACF nulls due to a single LFM pulse exactly at the positions of grating lobes. The scheme is valid if and only if Tr/T>2, which restricts the extent of increase in bandwidth by limiting the number of frequency steps for a signal of particular time duration. In the second part of the work presented in this thesis, a scheme has been proposed that allows to accommodate more bandwidth by taking Tr/T=1. It allows more number of pulses within the same signal time, and hence more number of frequency stepping to result a larger total bandwidth

Development of Radar Pulse Compression Techniques Using Computational Intelligence Tools

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