Nonlinear Estimation Techniques Applied on Target Tracking Problems.

This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observa-tions. These two target tracking problems provide a good bench-mark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its sta-bility and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters per-formances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accu-racy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlin-ear target tracking problems, which in the past have typically been solved by Kalman or particle filters. [ABSTRACT FROM AUTHOR]