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Optimal utilization of signal-free samples for array processing in unknown colored noise fields
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
2006 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 10, 3861-3872 p.Article in journal (Refereed) Published
Abstract [en]

Many algorithms for direction-of-arrival (DOA) estimation require the noise covariance matrix to be known or to possess a known structure. In many cases, the noise covariance is, in fact, estimated from separate measurements. This paper addresses the combined effects of finite sample sizes, both in the estimated noise covariance matrix and in the data with signals present. It is assumed that a batch of signal-free samples is available in addition to the signal-containing samples. No assumption is made on the structure of the noise covariance. In this paper, the asymptotic covariance of the weighted subspace fitting (WSF) algorithm is derived for the case in which the data are whitened using an estimated noise covariance. The expression obtained suggests an optimal weighting that improves performance compared to the standard choice. In addition, a new method based on covariance matching is proposed. Both methods are asymptotically statistically efficient. The Cramer-Rao lower bound (CRB) on the covariance of the estimate for the data model is also derived. Monte Carlo simulations show promising small sample performance for the two new methods and confirm,the asymptotic results.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2006. Vol. 54, no 10, 3861-3872 p.
Keyword [en]
colored noise, covariance matching, Cramer-Rao bound, direction-on-arrival (DOA) estimation, performance analysis, weighted subspace fitting, whitening errors, of-arrival estimation, maximum-likelihood-estimation, spatially correlated noise, subspace-based methods, sensor array, performance analysis, model errors, map approach, directions, music
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-16004DOI: 10.1109/tsp.2006.879324ISI: 000240775900018ScopusID: 2-s2.0-33749403179OAI: diva2:334046
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2012-02-11Bibliographically approved

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