Abstract
In this paper, an adaptation method for adjusting the scaling parameters of an unscented Kalman filter (UKF) is proposed to improve the estimation performance of the filter in dynamic conditions. The proposed adaptation method is based on a sequential algorithm that selects the scaling parameter using the user-defined distribution of discrete sets to more effectively deal with the changing measurement distribution over time and avoid the additional process for training a filter model. The adaptation method employs regularized optimal transport (ROT), which compensates for the error of the predicted measurement with the current measurement values to select the proper scaling parameter. In addition, the Sinkhorn–Knopp algorithm is used to minimize the cost function of ROT due to its fast convergence rate, and the convergence of the proposed ROT-based adaptive adjustment method is also analyzed. According to the analysis results of Monte Carlo simulations, it is confirmed that the proposed algorithm shows better performance than the conventional algorithms in terms of the scaling parameter selection in the UKF.
Highlights
Academic Editor: Stefano MarianiIn recent years, analysis of the state variables estimation of a dynamic system has played a central role in a variety of research areas such as navigation, target tracking, etc. [1,2,3,4,5,6,7]
Derivative-free approximations based on differential polynomial interpolations, such as an unscented transform (UT) and various numerical integration rules, were recently studied for nonlinear systems [12]
The unscented Kalman filter (UKF) is a representative filtering method of derivative-free approximations that employs selected sigma-points which are propagated through a known nonlinear system model and measurement model
Summary
Analysis of the state variables estimation of a dynamic system has played a central role in a variety of research areas such as navigation, target tracking, etc. [1,2,3,4,5,6,7]. To more effectively deal with the change of the measurement distribution in time and avoid the additional process for training a filter model, the proposed algorithm is designed with regularized optimal transport (ROT), which compensates for the error of predicted measurement with the current measurement values. It is based on the sequential algorithm that selects the scaling parameter using the user-defined discrete set of possible values.
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