Multistage partially adaptive STAP CFAR detection algorithm

Author

Goldstein, J.Scott ; Reed, Irving S. ; Zulch, Peter A.

Author_Institution

Lincoln Lab., MIT, Lexington, MA, USA

Volume

35

Issue

2

fYear

1999

fDate

4/1/1999 12:00:00 AM

Firstpage

645

Lastpage

661

Abstract

A new method of partially adaptive constant false-alarm rate (CFAR) detection is introduced. The processor implements a novel sequence of orthogonal subspace projections to decompose the Wiener solution in terms of the cross-correlation observed at each stage. The performance is evaluated using the general framework of space-time adaptive processing (STAP) for the cases of both known and unknown covariance. It is demonstrated that this new approach to partially adaptive STAP outperforms the more complex eigen-analysis approaches using both simulated DARPA Mountain Top data and true pulse-Doppler radar data collected by the MCARM radar

Keywords

Doppler radar; Wiener filters; adaptive filters; adaptive signal detection; correlation methods; covariance matrices; least mean squares methods; radar clutter; radar detection; radar signal processing; space-time adaptive processing; MMSE; Wiener solution decomposition; adaptive Wiener filtering; binary hypothesis problem; cross-correlation; data compression; hot clutter problem; known covariance; multistage partially adaptive STAP; orthogonal subspace projections; partially adaptive CFAR detection algorithm; radar signal processing; sidelobe cancelling filter; simulated data; space-time adaptive processing; tridiagonal covariance matrix; true pulse-Doppler radar data; unknown covariance; Adaptive signal detection; Aerospace testing; Bandwidth; Clutter; Detection algorithms; Interference; Laboratories; Matched filters; Radar scattering; Signal to noise ratio;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.766945

Filename

766945