Tracking the Orientation and Axes Lengths of an Elliptical Extended Object

TL;DR

This paper introduces a recursive Kalman filter method for accurately estimating an elliptical object's orientation and axes lengths in extended object tracking, addressing nonlinearity and high-dimensionality challenges.

Contribution

It provides compact closed-form expressions for a Kalman filter that explicitly estimates shape parameters, improving over existing Monte Carlo or implicit methods.

Findings

Outperforms state-of-the-art in simulations

Explicitly estimates orientation and axes lengths

Addresses nonlinearity and high-dimensionality

Abstract

Extended object tracking considers the simultaneous estimation of the kinematic state and the shape parameters of a moving object based on a varying number of noisy detections. A main challenge in extended object tracking is the nonlinearity and high-dimensionality of the estimation problem. This work presents compact closed-form expressions for a recursive Kalman filter that explicitly estimates the orientation and axes lengths of an extended object based on detections that are scattered over the object surface (according to a Gaussian distribution). Existing approaches are either based on Monte Carlo approximations or do not allow for explicitly maintaining all ellipse parameters. The performance of the novel approach is demonstrated with respect to the state-of-the-art by means of simulations.