Linear Convergence of distributed estimation with constraints and communication delays

Abstract

Motivated by the multi-sensor estimation problem, we consider a distributed constraint parameter estimation problem on a directed graph. Our goal is to design a fast distributed algorithm to handle constraint parameter estimation problems, especially considering the communication delays when sensors interact on a directed graph. Based on the gradient-tracking algorithm and indirect projection method, the Projected Distributed Parameter Estimation Algorithm (PDPEA) is proposed, which can deal with the closed convex set constraint and use the historical information stored by the sensors to achieve fast and accurate mean gradient estimation. The analysis shows that the PDPEA converges to a global and consensual minimizer even under the influence of measurement noise, arbitrarily bounded communication delays, and directed information topologies. Under the assumption that the local cost function is strongly convex and smooth, we show that the algorithm converges at a linear rate for an appropriate choice of stepsize. Lastly, we demonstrate the effectiveness of the proposed algorithm by setting up a multi-sensor parameter estimation network.