Robust Interacting Multiple Model With Modeling Uncertainties for Maneuvering Target Tracking

This paper proposes an improved robust interacting multiple model (RIMM) algorithms with modeling uncertainties for maneuvering target tracking with changing dynamics. To mitigate the effects of the modeling uncertainty, a compensation step is introduced to adjust the degree of dependence of the filtering on the system or the measurement model based on the orthogonality principle between the state estimation error and innovation sequence of the subfilter model in the RIMM algorithm. By relying on the compensation parameter, the proposed algorithm fully utilizes the useful information in the innovation sequence and reduces the impact of system model error. The numerical simulation and car-mounted experiments using time difference of arrival (TDOA) measurements of the maneuvering target tracking with changing dynamics are conducted to verify the effectiveness of the proposed RIMM algorithm. Compared with the conventional approaches, the proposed RIMM algorithm results in a remarkable improvement in the state estimation accuracy and small bias while improving the consistency of the filter.