2D and 3D Angles-Only Target Tracking Based on Maximum Correntropy Kalman Filters

Abstract

In this paper, angles-only target tracking (AoT) problem is investigated in the non Gaussian environment. Since the conventional minimum mean square error criterion based estimators tend to give poor accuracy in the presence of large outliers or impulsive noises in measurement, a maximum correntropy criterion (MCC) based framework is presented. Accordingly, three new estimation algorithms are developed for AoT problems based on the conventional sigma point filters, termed as MC-UKF-CK, MC-NSKF-GK and MC-NSKF-CK. Here MC-NSKF-GK represents the maximum correntropy new sigma point Kalman filter realized using Gaussian kernel and MC-NSKF-CK represents realization using Cauchy kernel. Similarly, based on the unscented Kalman filter, MC-UKF-CK has been developed. The performance of all these estimators is evaluated in terms of root-mean-square error (RMSE) in position and % track loss. The simulations were carried out for 2D as well as 3D AoT scenarios and it was inferred that, the developed algorithms performed with improved estimation accuracy than the conventional ones, in the presence of non Gaussian measurement noise.

The application of sliding innovation filter as estimation strategy applied to a UAV

“…This study is a direct application focused on estimating a UAV while it targets a flying object at the same altitude level. The paper partially relies on the model derived in [139]. The SIF is employed to estimate both the target and UAV (observer) trajectories, based on data from several sensors measuring the observer's location and the target's position relative to the UAV.…”