Abstract
Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the sensor network. This letter, in contrast, considers single time-scale distributed estimation via a sensor network subject to transmission time-delays. The proposed discrete-time networked estimator consists of two steps: (i) consensus on (delayed) a-priori estimates, and (ii) measurement update. The sensors only share their a-priori estimates with their out-neighbors over (possibly) time-delayed transmission links. The delays are assumed to be fixed over time, heterogeneous, and known. We assume distributed observability instead of local observability, which significantly reduces the communication/sensing loads on sensors. Using the notions of augmented matrices and the Kronecker product, the convergence of the proposed estimator over strongly-connected networks is proved for a specific upper-bound on the time-delay.
Highlights
L ATENCY in data transmission networks may significantly affect the performance of decision-making over sensor networks and multi-agent systems [1]
The literature on distributed estimation spans from multi time-scale scenarios to single time-scale methods. The former case requires many iterations of averaging/data-sharing between two consecutive system time-steps [7], [8], where the estimation performance tightly depends on the number of consensus iterations
Multi time-scale method, number of communication/consensus iterations is greater than the network diameter, and all sensors eventually gain all state information between every two system time-steps
Summary
L ATENCY in data transmission networks may significantly affect the performance of decision-making over sensor networks and multi-agent systems [1]. This work extends to distributed estimation over a sensor network with random communication time-delays. The literature on distributed estimation spans from multi time-scale scenarios to single time-scale methods The former case requires many iterations of averaging/data-sharing (consensus/communication time-scale) between two consecutive system time-steps (system time-scale) [7], [8], where the estimation performance tightly depends on the number of consensus iterations. Multi time-scale method, number of communication/consensus iterations is greater than the network diameter, and all sensors eventually gain all state information (and system observability) between every two system time-steps. The networked estimator in this paper is single time-scale, where sensors perform one consensus iteration on (possibly) delayed a-priori estimates in their in-neighborhood, and measurement-update using their own outputs. The optimal output selection strategies are of interest as in [25], [30], [31]
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