An Elliptical Principal Axes-based Model for Extended Target Tracking with Marine Radar Data

Abstract

Ellipses are favourable when it comes to tracking the shape of targets in a wide range of applications. With enhanced sensor technologies, the need for efficient measurement processing and accurate estimation keeps getting more pronounced. In this paper, we propose an approach called Principal Axes Kalman Filter (PAKF) for tracking an elliptical extended target whose extent parameters are estimated directly from explicit elliptical measurements (lengths of semi-axes and orientation), that have in turn been computed from a high number of (noisy) sensor measurements. The benefits of the approach, both in terms of processing and accuracy, are demonstrated by a comparison with two existing approaches: the random matrix model (RMM) and the Multiplicative Error Model-Extended Kalman Filter* (MEM-EKF*). Moreover, the approach is applied on a real-world standard on-board marine radar dataset and the outcomes are presented and discussed.