Distributed Unscented Estimation of Multi-Agent Systems with Homologous Unknown Inputs

This study focuses on the simultaneous estimation of unknown inputs (UIs) and states of nonlinear discrete-time heterogeneous multi-agent system with homologous UIs. Based on unscented Kalman filter (UKF), a minimum-variance unbiased filter for the UIs and state estimation is proposed for the multi-agent system with homologous UIs. Compared with previous studies, this paper proposes a general model that is applicable in practice. The neighbors' information of the homologous UIs are utilized to construct sigma points of the homologous UIs. In the case study, a target tracking problem is used to verify the advantage of the UKF-based distributed filter.