Date of Award

Fall 1995

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering - (M.S.)

Department

Electrical and Computer Engineering

First Advisor

Alexander Haimovich

Second Advisor

Hongya Ge

Third Advisor

Zoran Siveski

Abstract

Radar performance in heterogeneous clutter has been a much studied topic. In all the studies so far, various forms of the sample matrix inversion (SMI) technique where used to calculate the weight vector of the processor. In this thesis an eigenanalysis-based technique known as the eigencanceler, is used. Performance of this technique is compared to the performance of the generalized likelihood ration (GLR) processor. This comparison is done using the clutter edge model, in which there is an abrupt change in the clutter power in the reference window. It is shown that the false alarm rate fluctuations, of the cell averaging constant false alarm rate (CA-CFAR) eigencanceler, depend on the number of secondary data vectors used to estimate the covariance matrix. It is also shown that when the estimate of the covariance matrix is poor, the eigencanceler is able to perform where the GLR fails.

These two methods are also compared using the range-dependent clutter power model, in which the range clutter power is a Weibull random variable. It is shown that the performance of the eigencanceler depends heavily on the variance of the clutter power random variable. It is again shown that the eigencanceler is able to perform with a low number of range cell samples, where the GLR fails.

Share

COinS