An extended target tracking model with multiple random matrices and unified kinematics

TL;DR

This paper introduces a novel extended target tracking model using multiple random matrices and unified kinematics, improving data association efficiency and demonstrating superior performance through simulation.

Contribution

The paper proposes a new extended target tracking model with multiple subobjects and a gamma Gaussian inverse Wishart implementation, reducing data association complexity.

Findings

Model outperforms previous methods in simulations

Effective approximation reduces data association complexity

Demonstrates improved tracking accuracy

Abstract

This paper presents a model for tracking of extended targets, where each target is represented by a given number of elliptic subobjects. A gamma Gaussian inverse Wishart implementation is derived, and necessary approximations are suggested to alleviate the data association complexity. A simulation study shows the merits of the model compared to previous work on the topic.