The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	On the performance of minimum redundancy array for multisource direction finding
Author:	Lee, Byung-Seub
View Online:	njit-etd1990-035 ([vii], 205 pages ~ 4.4 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Doctor of Engineering Science
Program:	Electrical Engineering
Document Type:	Dissertation
Advisory Committee:	Bar-Ness, Yeheskel (Committee chair) Haimovich, Alexander (Committee member) Lu, Chung H. (Committee member) Tavantzis, John (Committee member)
Date:	1990
Keywords:	Signal processing. Estimation theory. Spectrum analysis. Power spectra.
Availability:	Unrestricted
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.