Variational Bayes for Robust Radar Single Object Tracking
- Resource Type
- Conference
- Authors
- Sari, Alp; Kaneko, Tak; Swaenen, Lense H.M.; Kouw, Wouter M.
- Source
- 2022 IEEE Workshop on Signal Processing Systems (SiPS) Signal Processing Systems (SiPS), 2022 IEEE Workshop on. :1-6 Nov, 2022
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Transportation
Filtering
Computational modeling
Spaceborne radar
Signal processing algorithms
Tail
Filtering algorithms
Radar imaging
Gaussian Sum Filter
Object Tracking
Radar
Robustness
t-distribution
Variational Bayes
- Language
- ISSN
- 2374-7390
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.