A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking

TL;DR

This paper introduces a Bayesian Rao-Blackwellized particle filter for tracking extended targets with superelliptical shapes, effectively handling measurements from their contours in complex scenarios.

Contribution

It presents a novel analytical framework for superelliptical shapes and a filtering algorithm that incorporates sensor-object geometry constraints for improved tracking.

Findings

High performance demonstrated in simulations

Effective in complex tracking scenarios

Handles various shapes like ellipses, rectangles, rhombi

Abstract

In this work, we propose a new method to track extended targets of different shapes such as ellipses, rectangles and rhombi. We provide an analytical framework to express these shapes as superelliptical contours and propose a Bayesian filtering scheme that can handle measurements from the contour of the object. The method utilizes the Rao-Blackwellized particle filtering algorithm with novel sensor-object geometry constraints. The success of the algorithm is demonstrated using both simulations and real-data experiments, and the algorithm has been demonstrated to be of high performance in various challenging scenarios.