Matrix analysis
Estimation of seismic-wave parameters and signal detection using maximum-likelihood methods
Computers & Geosciences
Time series: data analysis and theory
Time series: data analysis and theory
Broadband ML estimation under model order uncertainty
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Stochastic Maximum Likelihood Estimation Under Misspecified Numbersof Signals
IEEE Transactions on Signal Processing
Detection of the Number of Signals Using the Benjamini-Hochberg Procedure
IEEE Transactions on Signal Processing
Derivative-constrained frequency-domain wideband DOA estimation
Multidimensional Systems and Signal Processing
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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.