A Fast-converging Space-time Adaptive Processing Algorithm for Non-Gaussian Clutter Suppression

Abstract

The normalized fractionally-lower order moment (NFLOM) algorithm differs from the normalized least mean square (NLMS) algorithm in that it minimizes the lower order moment (p<2) of the error rather than the variance (p=2). This paper first evaluates the performances of the NFLOM for space-time adaptive processing in heavy-tailed compound K clutters in terms of the excess mean square error (MSE), misalignment, beampatterns, and output signal-to- interference-and-noise-ratio (SINR). the results show that the MSE curve of a small-order NFLOM exhibits faster convergence but higher steady-state error than a large-order NFLOM. Second, this paper proposes a new variable-order FLOM algorithm to dynamically change the order during adaptation, thus achieving both fast initial convergence and low steady-state error. the new algorithm is applied to STAP for Gaussian and non-Gaussian clutter suppression. the simulation results show that it achieves the best compromise between fast convergence and low steady-state error in both types of clutters. © 2010 Elsevier Inc. All Rights Reserved.

Department(s)

Electrical and Computer Engineering

Comments

Air Force Office of Scientific Research, Grant FA9550-07-1-0336

Keywords and Phrases

Adaptive filtering; Beamforming; Clutter suppression; Compound K distribution; Convergence; Normalized fractional lower order moment (NFLOM) algorithm; Normalized least mean square (NLMS) algorithm; Space-time adaptive processing (STAP)

International Standard Serial Number (ISSN)

1051-2004

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jan 2012

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