Kamthe, S;
Peters, J;
Deisenroth, MP;
(2014)
Multi-modal filtering for non-linear estimation.
In:
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
(pp. pp. 7979-7983).
IEEE: Florence, Italy.
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Abstract
Multi-modal densities appear frequently in time series and practical applications. However, they are not well represented by common state estimators, such as the Extended Kalman Filter and the Unscented Kalman Filter, which additionally suffer from the fact that uncertainty is often not captured sufficiently well. This can result in incoherent and divergent tracking performance. In this paper, we address these issues by devising a non-linear filtering algorithm where densities are represented by Gaussian mixture models, whose parameters are estimated in closed form. The resulting method exhibits a superior performance on nonlinear benchmarks.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-modal filtering for non-linear estimation |
| Event: | 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Location: | Florence, ITALY |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICASSP.2014.6855154 |
| Publisher version: | https://doi.org/10.1109/ICASSP.2014.6855154 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Acoustics, Engineering, Electrical & Electronic, Engineering, State estimation, Non-linear dynamical systems, Non-Gaussian filtering, Gaussian sum |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10083728 |
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