Performance Analysis of Direction Finding with Large Arrays and Finite Data
1992 (English)Report (Other academic)
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. The covariance matrix of the estimation error attains the Cramér-Rao bound. Under a certain assumption, the ML methods can be implemented by means of conventional beamforming for sufficiently large m. Surprisingly, this is shown to be possible also in the presence of perfectly correlated emitters.
Place, publisher, year, edition, pages
Linköping: Linköping University , 1992.
LiTH-ISY-I, ISSN 8765-4321 ; 1365
Estimation, Parameters, Narrowband signals, Maximum likelihood, Cramér-Rao bound
IdentifiersURN: urn:nbn:se:liu:diva-55553OAI: oai:DiVA.org:liu-55553DiVA: diva2:316367
FunderSwedish Research Council