Date of Award

9-1984

Degree Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical Engineering

Supervisor

Dr. S. Haykin

Abstract

A very rapidly convergent solution (in the form of a likelihood ratio test) for the problem of detecting a discrete-time stochastic process in additive white Gaussian noise is derived.

This likelihood ratio test is then applied to the problem of moving-target (aircraft) detection by airport surveillance radar systems. Using real radar data, the receiver operating characteristics are obtained for two different adaptive implementations of this likelihood ratio test, and also for the three versions of the Moving Target Detection algorithms presently in use in modern radar systems.

The better of the two adaptive implementations employs Kalman prediction error tapped delay-line filters and attains a minimum of 3 dB average performance improvement relative to the Moving Target Detection algorithms.