Quadratic Kalman Filter for Elliptical Extended Object Tracking based on Decoupling State Components

TL;DR

This paper introduces a deterministic elliptical extended object tracker that decouples state components for improved accuracy and efficiency, validated through simulations and real radar data.

Contribution

It proposes a novel decoupled approach for elliptical extended object tracking that simplifies estimation and enhances performance over existing methods.

Findings

Outperforms existing algorithms in accuracy.

Achieves high efficiency with a batch-based variant.

Validated on real automotive radar data.

Abstract

Extended object tracking involves estimating both the physical extent and kinematic parameters of a target object, where typically multiple measurements are observed per time step. In this article, we propose a deterministic closed-form elliptical extended object tracker, based on decoupling of the kinematics, orientation, and axis lengths. By disregarding potential correlations between these state components, fewer approximations are required for the individual estimators than for an overall joint solution. The resulting algorithm outperforms existing algorithms, reaching the accuracy of sampling-based procedures. Additionally, a batch-based variant is introduced, yielding highly efficient computation while outperforming all comparable state-of-the-art algorithms. This is validated both by a simulation study using common models from literature, as well as an extensive quantitative evaluation on real automotive radar data.