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Broadband ML estimation under model order uncertainty

P. J. Chung ; Mats Viberg (Institutionen för signaler och system, Signalbehandling) ; C. F. Mecklenbrauker
Signal Processing (0165-1684). Vol. 90 (2010), 5, p. 1350-1356.
[Artikel, refereegranskad vetenskaplig]

The number of signals hidden in data plays a crucial role in array processing. When this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. These methods are usually computationally intensive, since ML estimates are required for a hierarchy of nested models. In this contribution, we propose a computationally efficient solution to avoid this full search procedure and address issues unique to the broadband case. Our max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. Furthermore, we introduce a criterion based on the rank of the steering matrix to reduce indistinguishable components caused by overparameterization. Numerical experiments show that despite model order uncertainty, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense. (C) 2009 Elsevier B.V. All rights reserved.

Nyckelord: Direction of arrival, Broadband signals, Maximum likelihood estimation, Overparameterized models, Unknown number of signals, maximum-likelihood-estimation, signals, number



Denna post skapades 2010-04-08. Senast ändrad 2016-07-26.
CPL Pubid: 119377

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Elektroteknik och elektronik

Chalmers infrastruktur