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• W2645265072 abstract "In this paper we consider distributed estimation of an unknown Gaussian random variable with known mean and variance, where each sensor observation is affected by both multiplicative and additive Gaussian observation noises. We derive the corresponding Cramer Rao Lower Bound (CRLB) for both quantized and full precision observations. In sequel we provide some closed-form approximations for both CRLB expressions which provide us with better understanding of behavior of CRLBs. Afterwards through analytic and simulation results we report some scenarios that multiplicative observation noise can play an enhancive role in terms of estimation accuracy. We call this phenomena enhancement mode of multiplicative noise." @default.
• W2645265072 created "2017-06-30" @default.
• W2645265072 creator A5009259027 @default.
• W2645265072 creator A5090323125 @default.
• W2645265072 date "2017-03-01" @default.
• W2645265072 modified "2023-09-25" @default.
• W2645265072 title "Noise enhanced distributed Bayesian estimation" @default.
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• W2645265072 doi "https://doi.org/10.1109/icassp.2017.7952951" @default.
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