Uney, M;
Julier, SJ;
Maskell, S;
(2025)
Generalising Covariance Intersection for multiple posterior densities in multi-sensor fusion.
In:
2025 IEEE Statistical Signal Processing Workshop (SSP).
IEEE: Edinburgh, United Kingdom.
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Abstract
Covariance intersection (CI) is a widely studied method to combine posterior state probability densities in sensor fusion. CI combines two input densities by first considering their exponential mixture density (EMD), or weighted geometric mean density, followed by selecting the mixture weights. In this work, we first introduce Kullback-Leibler divergence centroid (KLDC) and minimum Entropy (ME) generalisations of CI to an arbitrary number of state densities. Then, we compare these two generalisations and the equally-weighted geometric average (EWGA) fusion, which is widely used in the literature, in scenarios with correlated measurements and clutter, or false alarms. Our experiments indicate that ME fusion performs the best in the case of highly correlated measurement errors or false alarms, whilst EWGA shows better performance in no to medium correlation with no false alarms.
| Type: | Proceedings paper |
|---|---|
| Title: | Generalising Covariance Intersection for multiple posterior densities in multi-sensor fusion |
| Event: | 2025 IEEE Statistical Signal Processing Workshop (SSP) |
| Dates: | 8 Jun 2025 - 11 Jun 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SSP64130.2025.11073391 |
| Publisher version: | https://doi.org/10.1109/ssp64130.2025.11073391 |
| 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: | Measurement errors, Correlation, Conferences, Signal processing algorithms, Signal processing, Sensor fusion, Entropy, Kalman filters, Clutter, Optimization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10217482 |
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