Distributed tracking with consensus on noisy time-varying graphs with incomplete data

摘要

In this paper, we formulate a problem of distributed tracking with consensus on a time-varying graph with incomplete data and noisy communication links. We develop a framework to handle a time-varying network topology in which not every node has local observations to generate own local tracking estimates (incomplete data). A distributed tracking-with-consensus algorithm that is suitable for such a noisy, time-varying graph is proposed. We establish the graph conditions so that distributed consensus can be achieved in the presence of noisy communication links when the effective network graph is time-varying. The steady-state performance of the proposed distributed tracking with consensus algorithm is also analyzed and compared with that of the distributed local Kalman filtering with the centralized fusion and centralized Kalman filter. Simulation results and performance analysis of the proposed algorithm are given, showing that the proposed distributed tracking with consensus algorithm performs almost the same as the distributed local Kalman filtering with centralized fusion on noisy time-varying graphs with incomplete data, while the proposed algorithm has the additional advantages of robustness and scalability.