This paper proposes an improved robust interacting multiple model (RIMM) algorithm 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 the 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. 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.

This paper proposes a novel adaptive Kalman filter (AKF) to estimate the unknown probability of measurement loss using the interacting multiple-model (IMM) filtering framework, yielding the IMM-AKF algorithm. In the proposed IMM-AKF algorithm, the state, Bernoulli random variable, and measurement loss probability are jointly inferred based on the variational Bayesian (VB) approach. In particular, a new likelihood definition is derived for the mode probability update process of the IMMAKF algorithm. Experiments demonstrate the superiority of the proposed IMM-AKF algorithm over existing filtering algorithms by adaptively estimating the unknown time-varying measurement loss probability.

This research used an invariant extended Kalman filter (IEKF) for the navigation of an unmanned aerial vehicle (UAV), and compared the properties and performance of this IEKF with those of an open-source navigation method based on an extended Kalman filter (EKF). The IEKF is a fairly new variant of the EKF, and its properties have been verified theoretically and through simulations and experiments. This study investigated its performance using a practical implementation and examined its distinctive features compared to the previous EKF-based approach. The test used two different types of UAVs: rotary wing and fixed wing. The method uses sensor measurements of the location and velocity from a GPS receiver; the acceleration, angular rate, and magnetic field from a microelectromechanical system-attitude heading reference system (MEMS-AHRS); and the altitude from a barometric sensor. Through flight tests, the estimated state variables and internal parameters such as the Kalman gain, state error covariance, and measurement innovation for the IEKF method and EKF-based method were compared. The estimated states and internal parameters showed that the IEKF method was more stable and convergent than the EKF-based method, although the estimated locations, velocities, and altitudes of the two methods were comparable.