A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

Abstract

Target tracking process consists of tracking and measuring a quantity over time, which is a difficult estimation problem. The latter gives erroneous measurements that are affected by the unknown number of targets to be tracked, as well as, measurements that are corrupted by noise. Thus, overcoming the target tracking problem includes basically solving the filtering problem which leads to a data association problem. Tracking problems that force lightly nonlinear models are solved appropriately with the Extended Kalman Filter. Although, problems that use nonlinear models, applying analytical expressions of the Jacobian of dynamic systems, or measurement vector functions are potentially solved by the Unscented Kalman Filter. The first technique is well built, but not precise, while the second technique yields very accurate results, but in terms of complexity, it is not suitable for real-time applications. Another filter called Particle Filter used a Monte Carlo method based on probability distribution over the state is proposed. Subsequently, we used a Gaussian Mixture Model which is based on the Expectation Maximization algorithm for fitting mixture-of-Gaussian models is considered to guarantee the estimation of optimal parameters set for each target. In order to achieve better trade between optimality and robustness of the tracking effect, the Smooth Variable Structure Filter is proposed as an alternative. This latter filter does not make any assumptions about noise characteristics and provides accurate estimation results. Furthermore, in this article, we propose a new target tracking algorithm based on the second form of Smooth Variable Structure Filter, which is implemented in a simulation using different scenarios. Experimental scenarios, under realistic conditions, are presented and the obtained results confirm the effectiveness of the Smooth Variable Structure Filter approach compared to other filters.

Combined Cubature Kalman and Smooth Variable Structure Filtering Based on Multi-Kernel Maximum Correntropy Criterion for the Fully Submerged Hydrofoil Craft

“…(4) Considering the MKMC, calculate the modified noise covariance R(k) by ( 43)-(55). ( 5) Then, considering the MKMC, and the modified measurement covariance in (21), the state error covariance of the SR-CKF in (27) and the state error covariance of the SVSF in (40) are used to replace the original measurement covariance for the robust to the heavy-tailed outliers.…”

“…The H∞ filter is designed by reducing the extreme scenario estimation discrepancies, while it is sensitive to the quantity of weighting functions and performance boundaries defined by users [17,18]. On the other hand, an innovative and robust estimation strategy termed as a smooth variable structure filter (SVSF) has been introduced [19][20][21]. It is a robust iterative estimation refinement method based on the smooth variable structure principles that enhances the robustness and stability by effectively managing modeling uncertainties.…”