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Performance analysis of direction finding with large arrays and finite data
Department of Applied Electronics, Chalmers University of Technology, Gothenburg, Sweden..
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
Department of Electrical Engineering, Yale University, New Haven, CT 06520 USA..
1995 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 43, no 2, 469-477 p.Article in journal (Refereed) Published
Abstract [en]

This paper considers analysis of methods for estimating the parameters of narrow-band signals arriving at an array of sensors. This problem has important applications in, for instance, radar direction finding and underwater source localization. The so-called deterministic and stochastic maximum likelihood (ML) methods are the main focus of this paper. A performance analysis is carried out assuming a finite number of samples and that the array is composed of a sufficiently large number of sensors. Several thousands of antennas are not uncommon in, e.g., radar applications. Strong consistency of the parameter estimates is proved, and the asymptotic covariance matrix of the estimation error is derived. Unlike the previously studied large sample case, the present analysis shows that the accuracy is the same for the two ML methods. Furthermore, the asymptotic covariance matrix of the estimation error coincides with the deterministic Cramer-Rao bound. Under a certain assumption, the ML methods can be implemented by means of conventional beamforming for a large enough number of sensors. We also include a simple simulation study, which indicates that both ML methods provide efficient estimates for very moderate array sizes, whereas the beamforming method requires a somewhat larger array aperture to overcome the inherent bias and resolution problem.

Place, publisher, year, edition, pages
IEEE Signal Processing Society , 1995. Vol. 43, no 2, 469-477 p.
Keyword [en]
Array signal processing, Covariance matrix, Estimation error, Maximum likelihood estimation, Narrow band, Parameter estimation, Performance analysis, Sensor array
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-53293DOI: 10.1109/78.348129ISI: A1995QG87500008OAI: oai:DiVA.org:kth-53293DiVA: diva2:469745
Note
QC 20111229Available from: 2011-12-27 Created: 2011-12-27 Last updated: 2017-12-08Bibliographically approved

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Ottersten, Björn

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