Broadband ML estimation under model order uncertainty
Artikel i vetenskaplig tidskrift, 2010

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.

Broadband signals

Direction of arrival



Unknown number of signals


Overparameterized models

Maximum likelihood estimation


P. J. Chung

University of Edinburgh

Mats Viberg

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Christoph F. Mecklenbräuker

Technische Universität Wien

Signal Processing

0165-1684 (ISSN)

Vol. 90 5 1350-1356


Elektroteknik och elektronik



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