Variational Bayes for robust radar single object tracking
This work addresses robustness in radar tracking for applications like autonomous systems, but it is incremental as it modifies an existing baseline method.
The paper tackled the problem of robust single object tracking in radar by addressing the sensitivity of standard Kalman filters to process outliers, proposing a modification using heavier-tailed noise distributions and variational Bayes inference, with simulations showing improved performance over the Gaussian Sum filter in outlier scenarios.
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.