Stochastic ML Estimation in Sensor Arrays by Weighted Subspace Fitting
1989 (English)Report (Other academic)
The problem of estimating parameters of multiple narrowband emitter signals from sensor array data is considered. Under the assumption of Gaussian distributed emitter signals, the stochastic maximum-likelihood (ML) technique is known to provide statistically efficient estimates, i.e., it achieves the Cramer-Rao bound (CRB). A multidimensional signal subspace method, termed weighted subspace fitting (WSF), has recently been proposed. It is shown that the WSF and ML estimates are asymptotically identical (for large data records). As a consequence, the WSF method is asymptotically efficient, assuming temporally white Gaussian signal waveforms and noise.
Place, publisher, year, edition, pages
Linköping: Linköping University , 1989. , 5 p.
LiTH-ISY-I, ISSN 8765-4321 ; 1042
Probability, Sensors, Cramer-Rao Bound, Maximum Likelihood Estimation, Weighted Subspace Fitting, Control Systems
IdentifiersURN: urn:nbn:se:liu:diva-104059OAI: oai:DiVA.org:liu-104059DiVA: diva2:694485