Contributions to Proceedings:
P.J. Chung, M. Viberg, C. Mecklenbräuker:
"Broadband ML Estimation Under Model Order Uncertainty";
in: "International Conference on Acoustics, Speech, and Signal Processing",
The number of signals plays a crucial role in array processing. The performance of most direction ﬁnding algorithms relies strongly on a correctly speciﬁed number of signals. 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 the previous work , we proposed a computationally efﬁcient solution to avoid this full search procedure and demonstrated its feasibility by extensive simulations. Here we extend  to broadband data, 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. Another novelty of this work is the reduction of indistinguishable components caused by overparameterization. Our approach is based on the rank of the estimated steering matrix. Numerical experiments show that despite an unknown number of signals, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense.
broadband signals, maximum likelihood estimation, direction of arrival, unknown number of signals, overparameterized models
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Project Head Gerald Matz:
Signal and Information Processing in Science and Engineering - Informationsnetze
Created from the Publication Database of the Vienna University of Technology.