Abstract�ESPRIT is a recently developed and patented technique for high-resolution estimation of signal parameters. It exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, and in addition achieves performance improvement as measured by parameter estimate error variance. It is also manifestly more robust with respect to sensor errors (e.g. gain, phase, and location errors) than other methods as well. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering two identical but other-wise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform linear arrays of identical sensor elements. Phased array radars are commonplace in high-resolution direction finding systems, and uniform tapped delay lines (i.e., constant rate A/D converters) are the rule rather than the exception in digital signal processing systems. Such arrays possess many invariances, and are amenable to other types of analysis, which is one of the main reasons such structures are so prevalent. Recent developments in high-resolution algorithms of the signal/noise subspace genre including total least squares (TLS) ESPRIT applied to uniform linear arrays are summarized. ESPRIT is also shown to be a generalization of the root-MUSIC algorithm (applicable only to the case of uniform linear arrays of omni-directional sensors and unimodular cisoids). Comparisons with various estimator bounds, including CramerRao bounds, are presented.

Date Published: 14 November 1989
PDF: 12 pages
Proc. SPIE 1152, Advanced Algorithms and Architectures for Signal Processing IV, (14 November 1989); doi: 10.1117/12.962293

Published in SPIE Proceedings Vol. 1152:
Advanced Algorithms and Architectures for Signal Processing IV
Franklin T. Luk, Editor(s)