Distributed Filtering for Multi-Agent Systems With Time-Varying Range Constraints

The existing studies on distributed Kalman filters (DKFs) mainly focus on linear constraints, thereby restricting their practical applications. This article investigates the distributed state estimation for nonlinear multiagent systems with time-varying range constraints. By applying the variable splitting technique and the scaled alternating direction method of multipliers, a distributed extended Kalman filter is proposed for the iterative state estimation under range constraints. Furthermore, sufficient conditions are given to ensure that the proposed distributed filtering algorithm satisfies all the range constraints. Finally, the effectiveness of the state estimation algorithm is demonstrated through numerical simulations and practical experiments of multi-agent tracking.