Abstract
In target tracking, the tracking process needs to constantly update the data information. For maneuvering target, model mismatch and loss of high-order moment information disrupt the accuracy of the state estimation. In this paper, an adaptive high-order unscented Kalman filter (AHUKF) algorithm is proposed for the case of errors occurring in the capturing of model dynamic behavior using the classical unscented Kalman filter (UKF) algorithm. By introducing the free parameter, the analytical solution of the high-order unscented transformation (UT) was obtained, the basis for choosing free parameters was analyzed, and the stability of the algorithm was discussed. A method for obtaining the optimal adaptive factor based on the prediction residual estimation covariance matrix was proposed, which reduces the influence of the dynamic model error and was applied to the target tracking model. In this paper, the proposed AHUKF is applied to a target tracking problem with state mutation, different sampling intervals, and different turn rates, respectively. Simulation results for target tracking illustrate that the proposed algorithm is more accurate and robust than the UKF, high-order unscented Kalman filter (HUKF) and adaptive unscented Kalman filter (AUKF).
Highlights
In recent years, the study of dynamic systems has been widely used in different fields
Nonlinear filtering is an advantageous method used to deal with dynamic systems, which plays an important role in target tracking, integrated navigation, positioning, control, and signal processing [1]–[8]
DETAILED ALGORITHM FOLLOWS We summarized the implementation of the improved adaptive high-order unscented Kalman filter (AHUKF) algorithm as follows: 1) The initial value is given by equation (3) and the state vector xk−1 is assumed from xk−1 ∼ N
Summary
The study of dynamic systems has been widely used in different fields. UKF is essentially a nonlinear filtering method based on second-order UT, which can only match the second-order moment of Taylor series expansion of nonlinear functions, so the error is limited, and the accuracy needs to be improved. The accuracy improvements depend on the matching degree of covariance To solve this problem, an adaptive adjustment factor based on the residual vector was introduced to reduce the weight of covariance of a filter in the stationary state and to further adjust the influence of the gain matrix on the system. The key contributions of this paper are expressed as follows: First of all, the defects of standard UKF sigma points sampling method are analyzed, a high-order sampling strategy is proposed to match the probability distribution of the state to improve the accuracy of target tracking.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
