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Weighted subspace fitting for general array error models
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-6855-5868
Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602 USA.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
1998 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 46, no 9, 2484-2498 p.Article in journal (Refereed) Published
Abstract [en]

Model error sensitivity is an issue common to all high-resolution direction-of-arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to army calibration errors have also appeared in the literature. Herein, one such approach is adopted that assumes that the errors due to finite samples and model errors are of comparable size. A weighted subspace fitting method for very general array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly, the method reduces to the WSF (MODE) estimator if no model errors are present, Vice versa, assuming that model errors dominate, the method specializes to the corresponding "model-errors-only subspace fitting method." Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated. The paper also contains a large sample analysis of one of the alternative methods, namely, MAPprox, It is shown that MAPprox also provides minimum variance estimates under reasonable assumptions.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 1998. Vol. 46, no 9, 2484-2498 p.
Keyword [en]
Antenna arrays, array signal processing, direction-of-arrival estimation, error analysis, parameter estimation, robustness.
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-60024DOI: 10.1109/78.709536ISI: 000075544300018OAI: diva2:477146
NR 20140805Available from: 2012-01-12 Created: 2012-01-12 Last updated: 2012-02-11Bibliographically approved

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