Variational Bayesian Inference for Multiple Extended Targets or Unresolved Group Targets Tracking

TL;DR

This paper introduces a novel variational Bayesian approach for tracking multiple extended or group targets in cluttered environments, utilizing the Random Matrix Model and probabilistic data association to improve accuracy and computational efficiency.

Contribution

The work presents a new variational Bayesian inference method for multiple target tracking that incorporates the Gamma Gaussian Inverse Wishart distribution and offers lightweight schemes for practical implementation.

Findings

Outperforms existing methods in accuracy and adaptability

Effective in cluttered environments with multiple targets

Provides computationally efficient schemes for real-time tracking

Abstract

In this work, we propose a method for tracking multiple extended targets or unresolvable group targets in a clutter environment. Firstly, based on the Random Matrix Model (RMM), the joint state of the target is modeled as the Gamma Gaussian Inverse Wishart (GGIW) distribution. Considering the uncertainty of measurement origin caused by the clutters, we adopt the idea of probabilistic data association and describe the joint association event as an unknown parameter in the joint prior distribution. Then the Variational Bayesian Inference (VBI) is employed to approximately solve the non-analytical posterior distribution. Furthermore, to ensure the practicability of the proposed method, we further provide two potential lightweight schemes to reduce its computational complexity. One of them is based on clustering, which effectively prunes the joint association events. The other is a simplification of the variational posterior through marginal association probabilities. Finally, the effectiveness of the proposed method is demonstrated by simulation and real data experiments, and we show that the proposed method outperforms current state-of-the-art methods in terms of accuracy and adaptability.