摘要
In this paper,a distributed stochastic approximation algorithm is proposed to track the dynamic root of a sum of time-varying regression functions over a network.Each agent updates its estimate by using the local observation,the dynamic information of the global root,and information received from its neighbors.Compared with similar works in optimization area,we allow the observation to be noise-corrupted,and the noise condition is much weaker.Furthermore,instead of the upper bound of the estimate error,we present the asymptotic convergence result of the algorithm.The consensus and convergence of the estimates are established.Finally,the algorithm is applied to a distributed target tracking problem and the numerical example is presented to demonstrate the performance of the algorithm.
基金
This work was supported by the National Key Research and Development Program of China under Grant 2018YFA0703800
the National Natural Science Foundation of China under Grant 61822312
This work was also supported(in part)by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27000000.
