Array Processing in Correlated Noise Fields Based on Instrumental Variables and Subspace Fitting
1995 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 43, no 5, 1187-1199 p.Article in journal (Refereed) Published
Accurate signal parameter estimation from sensor array data is a problem which has received much attention in the last decade. A number of parametric estimation techniques have been proposed in the literature. In general, these methods require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. This difficulty can be overcome only by introducing alternative assumptions that enable separating the signals from the noise. In some applications, the raw sensor outputs can be preprocessed so that the emitter signals are temporally correlated with correlation length longer than that of the noise. An instrumental variable (IV) approach can then be used for estimating the signal parameters without knowledge of the spatial color of the noise. A computationally simple IV approach has recently been proposed by the authors. Herein, a refined technique that can give significantly better performance is derived. A statistical analysis of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities. A lower bound on the attainable error variance is also presented. The proposed optimal IV method is shown to attain the bound if the signals have a quasideterministic character
Place, publisher, year, edition, pages
1995. Vol. 43, no 5, 1187-1199 p.
Array processing, instrumental variable (IV) approach, correlation methods, covariance matrices, direction-of-arrival estimation, noise, singular value decomposition, statistical analysis
IdentifiersURN: urn:nbn:se:kth:diva-53291DOI: 10.1109/78.382403ISI: A1995QX51100013OAI: oai:DiVA.org:kth-53291DiVA: diva2:469743
QC 201201042011-12-272011-12-272012-01-04Bibliographically approved