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Interacting Multiple Model Estimation-based Adaptive Robust Unscented Kalman Filter

Bingbing Gao*, Shesheng Gao, Yongmin Zhong, Gaoge Hu, and Chengfan Gu
International Journal of Control, Automation, and Systems, vol. 15, no. 5, pp.2013-2025, 2017

Abstract : "The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamic systems due to its simple calculation process and superior performance in highly nonlinear systems. However, its solution will be degraded or even divergent when the system model involves uncertainty. This paper presents an interacting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This method combines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbance of system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process model uncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principle of innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF and robust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilistic weighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparison analysis validate the efficacy of the proposed method."

Keyword : "Adaptive fading factor, interacting multiple model, robust factor, system model uncertainty, unscented Kalman filter."

 
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