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Date of Award

2009

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Electrical and Computer Engineering

Supervisor

Thia Kirubarajan

Language

English

Abstract

In radar signal processing, the vulnerability of the desired signal to homogeneous and heterogeneous interferences increases as the communication traffic increases. The principal challenge in the radar system then is to mitigate the effects of cold (homogeneous) clutter, severe dynamic (heterogeneous) hot clutter and jamming interferences while estimating the states of targets under track. Space-Time Adaptive Processing (STAP) enhances the capability of radar systems to overcome this challenge. However, it is a sample-based system where the adaptive processing is sensitive to the underlying assumptions as well as the diversity of potential interferences. Hence, the performance of STAP deteriorates when basic assumptions are violated due to errors in receiver array elements, non-stationary nature of interferences, inadequate Independent and Identically Distributed (i.i.d.) sample data, and target like-signal in the training data set.

This thesis proposes an Adaptive State Estimation (ASE) approach to characterize STAP used simultaneously in spatial and Doppler domains for non-stationary, homogeneous and heterogeneous systems. The contributions presented here are based on the adjustment of the weight vector and the update of associated interference covariance matrix by ASE to minimize the output noise power while maximizing Signal to Interference-plus-Noise Ratio (SINR) in the Mean Squared Error (MSE) sense. The integration of STAP principle with sequential state estimation in order to decode the target signal while rejecting the interferences due to non-stationary heterogeneous clutter and jammer effects without degrading performance is the key contribution of this paper. The Proposed STAP-ASE algorithm is shown to outperform its counterparts in terms of efficiency, IF-improvement factor, Signal to Interference-plus-Noise Ratio (SINR) convergence rate and target detection. Simulation results are presented to illustrate the performance of the proposed technique.

McMaster University Library

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