Variational Bayes for robust radar single object tracking

TL;DR

This paper introduces a robust radar object tracking method using Variational Bayes to model process noise with heavy tails, improving performance during abrupt motions and outliers.

Contribution

It proposes a novel modification of the Gaussian Sum Filter by incorporating heavy-tailed noise modeling via Variational Bayes for enhanced robustness.

Findings

Robust tracker outperforms Gaussian Sum filter with outliers

Heavy-tailed noise modeling improves tracking during abrupt motions

Simulation results confirm effectiveness of the proposed method

Abstract

We address object tracking by radar and the robustness of the current state-of-the-art methods to process outliers. The standard tracking algorithms extract detections from radar image space to use it in the filtering stage. Filtering is performed by a Kalman filter, which assumes Gaussian distributed noise. However, this assumption does not account for large modeling errors and results in poor tracking performance during abrupt motions. We take the Gaussian Sum Filter (single-object variant of the Multi Hypothesis Tracker) as our baseline and propose a modification by modelling process noise with a distribution that has heavier tails than a Gaussian. Variational Bayes provides a fast, computationally cheap inference algorithm. Our simulations show that - in the presence of process outliers - the robust tracker outperforms the Gaussian Sum filter when tracking single objects.