Reduced-rank STAR performance analysis

Document Type

Article

Publication Date

4-1-2000

Abstract

The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank (RR) methods outperform full-rank space-time adaptive processing (STAP) when the space-time covariance matrix is estimated from a data set with limited support. The utility of RR methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that RR processing has two opposite effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio (SNR). A method for evaluating the theoretical conditioned SNR for fixed RR transforms is also presented. It is shown that while best performance is obtained using data-dependent transforms, the loss incurred by the application of fixed transforms (such as the discrete cosine transform) may be relatively small. The main advantage of fixed transforms is the availability of efficient computational procedures for their implementation. These findings suggest that RR methods could facilitate the development of practical, real-time STAP technology. © 2000 IEEE.

Identifier

0033745744 (Scopus)

Publication Title

IEEE Transactions on Aerospace and Electronic Systems

External Full Text Location

https://doi.org/10.1109/7.845257

ISSN

00189251

First Page

664

Last Page

676

Issue

2

Volume

36

This document is currently not available here.

Share

COinS