Abstract
In the actual application process, the impact of the surrounding environment, the error of the observation device, and the improper parameter selection usually yield the system error. The incremental Kalman filter can effectively solve the state estimation problem for the systems under poor observation condition. In this paper, a distributed fusion incremental Kalman filtering algorithm is presented, which can eliminate the unknown system errors and improve the accuracy of state estimators for the multi-sensor systems under poor observation condition. It is simple in form and easy to be applied in engineering practice. The simulation results show its effectiveness and feasibility.
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