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Modified IQML and Weighted Subspace Fitting without Eigendecomposition
KTH, Superseded Departments, Signals, Sensors and Systems.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-6855-5868
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
1999 (English)In: Signal Processing, ISSN 0165-1684, Vol. 79, no 1, 29-44 p.Article in journal (Refereed) Published
Abstract [en]

This paper deals with direction estimation of signals impinging on a uniform linear sensor array. A well-known algorithm for this problem is IQML. However, estimates computed with IQML are in general biased, especially in noisy scenarios. We propose a modification of IQML (MIQML) that gives consistent estimates at approximately the same computational cost. In addition, an algorithm (WSF-E) with an estimation error covariance which is asymptotically identical to the asymptotic Cramér–Rao lower bound is presented. The WSF-E algorithm resembles weighted subspace fitting (WSF) or MODE, but achieves optimal performance without having to compute an eigendecomposition of the sample covariance matrix.

Place, publisher, year, edition, pages
Elsevier North-Holland, Inc. Amsterdam, The Netherlands , 1999. Vol. 79, no 1, 29-44 p.
Keyword [en]
Direction estimation; Linear array; Maximum likelihood; Subspace methods; Eigendecomposition; Low complexity
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-59759DOI: 10.1016/S0165-1684(99)00078-XOAI: oai:DiVA.org:kth-59759DiVA: diva2:476228
Note
NR 20140805Available from: 2012-01-11 Created: 2012-01-11 Last updated: 2012-02-11Bibliographically approved

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Jansson, MagnusOttersten, Björn

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