Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria

Details Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria

2005-01-22

arxiv.org/abs/cs/0501058v1

Computer Science

Information Theory

To appear in the IEEE Transactions on Signal Processing

Scientific paper

10.1109/TSP.2005.853099

Estimating the number of sources impinging on an array of sensors is a well known and well investigated problem. A common approach for solving this problem is to use an information theoretic criterion, such as Minimum Description Length (MDL) or the Akaike Information Criterion (AIC). The MDL estimator is known to be a consistent estimator, robust against deviations from the Gaussian assumption, and non-robust against deviations from the point source and/or temporally or spatially white additive noise assumptions. Over the years several alternative estimation algorithms have been proposed and tested. Usually, these algorithms are shown, using computer simulations, to have improved performance over the MDL estimator, and to be robust against deviations from the assumed spatial model. Nevertheless, these robust algorithms have high computational complexity, requiring several multi-dimensional searches. In this paper, motivated by real life problems, a systematic approach toward the problem of robust estimation of the number of sources using information theoretic criteria is taken. An MDL type estimator that is robust against deviation from assumption of equal noise level across the array is studied. The consistency of this estimator, even when deviations from the equal noise level assumption occur, is proven. A novel low-complexity implementation method avoiding the need for multi-dimensional searches is presented as well, making this estimator a favorable choice for practical applications.

Affiliated with

Also associated with

No associations

Rating

Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-279621