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Distributed Unscented Kalman Filters for Nonlinear 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 UIs and states are utilized to construct sigma points of the UKF. Moreover, the updating method of the proposed filter is improved. In the case study, a target tracking problem is used to verify the advantage of the UKF-based distributed filter.