Systems & Control Letters
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distortion-rate bounds for distributed estimation using wireless sensor networks
EURASIP Journal on Advances in Signal Processing
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
The marginal likelihood for parameters in a discrete Gauss-Markovprocess
IEEE Transactions on Signal Processing
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
Brief Regularization networks for inverse problems: A state-space approach
Automatica (Journal of IFAC)
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This paper considers the state-space smoothing problem in a distributed fashion. In the scenario of sensor networks, we assume that the nodes can be ordered in space and have access to noisy measurements relative to different but correlated states. The goal of each node is to obtain the minimum variance estimate of its own state conditional on all the data collected by the network using only local exchanges of information. We present a cooperative smoothing algorithm for Gauss-Markov linear models and provide an exact convergence analysis for the algorithm, also clarifying its advantages over the Jacobi algorithm. Extensions of the numerical scheme able to perform field estimation using a grid of sensors are also derived.