Direction Finding with the Sensors' Gains Suffering Bayesian Uncertainty — Hybrid CRB and MAP Estimation

dc.contributor.authorYue, Ivan Wu
dc.contributor.authorKitavi, Dominic M.
dc.contributor.authorLin, Tsair-Chuan
dc.contributor.authorWong, Kainam Thomas
dc.date.accessioned2018-07-10T09:50:12Z
dc.date.available2018-07-10T09:50:12Z
dc.date.issued2016-08
dc.description.abstractThe paper analyzes how a sensor array's direction-finding accuracy may be degraded by any stochastic uncertainty in the sensors' complex value gains, modeled here as complex value Gaussian random variables. This analysis is via the derivation of the hybrid Cramer-Rao bound (HCRB) of the azimuth-elevation direction-of-arrival estimates. This HCRB is analytically shown to be inversely proportional to a multiplicative factor equal to one plus the variance of the sensors' gain uncertainty. This finding applies to any array grid geometry. The maximum a posteriori (MAP) estimator corresponding to this uncertain gain data model is also derived. Monte Carlo simulations demonstrate that this estimator approaches the lower bound derived.en_US
dc.identifier.citationIEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 4, pp. 2038 – 2044en_US
dc.identifier.issn0018-9251
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/7738373/
dc.identifier.uriDOI: 10.1109/TAES.2016.150193
dc.identifier.urihttp://hdl.handle.net/123456789/1756
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectSensor arrayen_US
dc.subjectStochastic processesen_US
dc.subjectUncertaintyen_US
dc.subjectData modelsen_US
dc.subjectCovariance matricesen_US
dc.titleDirection Finding with the Sensors' Gains Suffering Bayesian Uncertainty — Hybrid CRB and MAP Estimationen_US
dc.typeArticleen_US
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