ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Model order selection of damped sinusoids in noise by predictivedensities
IEEE Transactions on Signal Processing
An information theoretic approach to source enumeration in array signal processing
IEEE Transactions on Signal Processing
Source number estimators using transformed Gerschgorin radii
IEEE Transactions on Signal Processing
On the behavior of information theoretic criteria for model orderselection
IEEE Transactions on Signal Processing
Signal Processing Techniques for Robust Speech Recognition
IEICE - Transactions on Information and Systems
Tracking intermittently speaking multiple speakers using a particle filter
EURASIP Journal on Audio, Speech, and Music Processing
Multisource self-calibration for sensor arrays
IEEE Transactions on Signal Processing
Efficient source enumeration for accurate direction-of-arrival estimation in threshold region
Digital Signal Processing
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High-resolution methods for estimating signal processing parameters such as bearing angles in array processing or frequencies in spectral analysis may be hampered by the model order if poorly selected. As classical model order selection methods fail when the number of snapshots available is small, this paper proposes a method for noncoherent sources, which continues to work under such conditions, while maintaining low computational complexity. For white Gaussian noise and short data we show that the profile of the ordered noise eigenvalues is seen to approximately fit an exponential law. This fact is used to provide a recursive algorithm which detects a mismatch between the observed eigenvalue profile and the theoretical noise-only eigenvalue profile, as such a mismatch indicates the presence of a source. Moreover this proposed method allows the probability of false alarm to be controlled and predefined, which is a crucial point for systems such as RADARs. Results of simulations are provided in order to show the capabilities of the algorithm.