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Article Dans Une Revue Digital Signal Processing Année : 2021

Distributed Bayesian vector estimation using noise-optimized low-resolution sensor observations

Jia Liu
Fabing Duan
Derek Abbott

Résumé

The distributed Bayesian vector parameter estimation problem based on low-resolution observations is investigated in a network, where each node represents an ensemble of estimates from a large number of sensors. A noise-enhanced Bayesian vector estimator that benefits from artificially added noise is proposed. For a network node composed of a sufficiently large number of identical low-resolution sensors, a lemma governing the weight coefficients is proven, and low-cost calculation expressions of the designed estimator and its Bayesian mean square error (MSE) are derived by avoiding costly computations due to high-dimensional matrix inversions. Experimental results show that by intentionally adding an appropriate amount of noise to networks of the low-resolution sensors, the MSE of the designed Bayesian vector estimator can be significantly reduced.
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Dates et versions

hal-03343189 , version 1 (14-09-2021)

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Jia Liu, Fabing Duan, François Chapeau-Blondeau, Derek Abbott. Distributed Bayesian vector estimation using noise-optimized low-resolution sensor observations. Digital Signal Processing, 2021, 118, pp.103224,1-10. ⟨10.1016/j.dsp.2021.103224⟩. ⟨hal-03343189⟩
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