Distributed Nonlinear State Estimation for Multiagent Systems Under State Constraints and Limited Communication

Existing studies on distributed filtering for nonlinear state-space models with state constraints have often overlooked communication limitations, which are essential for practical implementation. To investigate the interplay between state constraints and communication limitations, a projected distributed extended Kalman filter (DEKF) is proposed. The proposed framework employs an event-triggered (ET) mechanism to reduce the communication load, while filter gains are derived by minimizing an upper bound of the projection error covariance. The proposed method ensures the exponential asymptotic unbiasedness of the state estimation under relaxed assumptions. Furthermore, sufficient conditions are derived to guarantee the boundedness of the projection error covariance under the ET scheme, which further enables a triggered-threshold design. The effectiveness of the proposed method is validated through real-world ground vehicle formation experiments.