1. Variational Bayes for robust radar single object tracking
- Author
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Sarı, Alp, Kaneko, Tak, Swaenen, Lense H. M., Kouw, Wouter M., Electrical Engineering, Signal Processing Systems, and Bayesian Intelligent Autonomous Systems
- Subjects
Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Radar ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,t-distribution ,Machine Learning (cs.LG) ,Object Tracking ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Signal Processing ,Gaussian Sum Filter ,Robustness ,Variational Bayes - 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., Comment: 6 pages, 8 figures. Published as part of the proceedings of the IEEE International Workshop on Signal Processing Systems 2022
- Published
- 2022