A Sequential Estimation Approach for Performance Improvement of Eigenstructure-Based Methods in Array Processing

• Published in: IEEE transactions on signal processing Vol. 41; no. 1; pp. 457 - 463
• Main Authors: Oh, Seong Keun, Un, Chong Kwan
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.1993
• Subjects: Direction of arrival estimation; Interpolation; Iterative algorithms; Maximum likelihood estimation; Multiple signal classification; Narrowband; Sensor arrays; Signal processing; Signal processing algorithms; Speech processing
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.1993.193178

In this correspondence, we present a simple and noniterative approach that lowers noticeably the threshold signal-to-noise ratio (SNR) of the eigenstructure-based techniques for estimating the directions of arrival (DOA's) of multiple narrow-band sources in passive sensor arrays. This approach removes effectively the spatial interferences among sources through the sequential estimation of the DOA's. We develop a sequential multiple signal classification (MUSIC) algorithm by applying the approach to the MUSIC algorithm, although it can be equally well applicable to other eigenstructure-based methods. We also present a recursive computational procedure (RCP) that reduces significantly the computational complexity of the proposed algorithm by transforming the computation of Hermitian forms into that of only inner products of vectors. Computer simulation results that demonstrate the resolution performance of the proposed algorithm are included. Since the algorithm is simple and noniterative, and also provides high resolution, it may be used on its own, or be used to provide good initial estimates for more complex iterative algorithms.