Distributed Agent-based Dynamic State Estimation over a Lossy Network

In this paper, a novel distributed agent-based dynamical system estimation strategy is proposed. Each agent has a local observation space and is interested in a specific set of system state elements. The agents have the ability of two-way communication with its neighbors (i.e., agents who share at least one state element). At a particular time instant, each agent predicts its state and makes intermediate correction based on its local measurements. Information about the corrected state elements are then exchanged among the neighboring agents. Based on the final processing of these exchanged information, an agent-based Kalman consensus Filter (AKCF) and uniform weighting based diffusion Kalman filter (ADKF) are proposed in the light of well-established theory of distributed Kalman filtering. Two different systems are simulated using the proposed filters. The effect of communication is also investigated by introducing random failures in the communication link among neighboring agents. It is observed that the mean square deviation (MSD) of AKCF is lower than that of ADKF for the scenarios considered. Additionally, the results also demonstrate that the AKCF is more robust to communication link failures than the ADKF.