Date of Award

Spring 1990

Document Type

Dissertation

Degree Name

Doctor of Engineering Science in Electrical Engineering

Department

Electrical and Computer Engineering

First Advisor

Yeheskel Bar-Ness

Second Advisor

Alexander Haimovich

Third Advisor

Chung H. Lu

Fourth Advisor

John Tavantzis

Abstract

As an application of power spectrum estimation, the multi-source direction finding has been evolved from conventional FFT method to Superresolution methods such as Multiple Signal Classification(MUSIC) algorithm. Uniform Regular Array(URA) was mainly used in all these approaches.

The Minimum Redundancy array(MRA); a non-uniform thinned array which results in an input signals covariance matrix with minimum redundancy has been shown to have certain interesting properties for spectrum estimation. Only recently it was suggested to use the MRA for spatial estimation. The purpose of this research was to study the performance of this array in multi-source direction finding estimation and compare it to the result obtained with URA. Although the emphasis in this research is on using the popular MUSIC algorithm, other algorithms are also considered.

Among the topics related to the MRA performance studied in the course of this research are

1. Effect of random displacement of the array element location on the performance of multi-source direction finding.

2. Performance of the MRA versus the URA using MUSIC and Minimum-Norm algorithms.

3. Performance of the MUSIC based direction finding using different covariance matrix estimates for URA and MRA.

4. The error probability of estimating the number (two in particular) of closely located sources with MRA versus URA.

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