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

Abstract 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.