| Paper: | SPTM-L1.6 | ||
| Session: | Bayesian Approaches and Particle Filters | ||
| Time: | Tuesday, May 16, 12:10 - 12:30 | ||
| Presentation: | Lecture | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, Classification Theory and Applications | ||
| Title: | A Modified Rao-Blackwellised Particle Filter | ||
| Authors: | Frédéric Mustière, Miodrag Bolic, Martin Bouchard, University of Ottawa, Canada | ||
| Abstract: | Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle Filters (PFs) that exploit conditional dependencies between parts of the state to estimate. By doing so, RBPFs can improve the estimation quality while also reducing significantly the computational complexity in comparison to original PFs. However, the computational load is still too high for many real-time applications. In this paper, we propose a modified RBPF that requires a single KF iteration per input sample. Comparative experiments show that while good convergence can still be obtained, computational efficiency is always drastically increased, making this algorithm an option to consider for real-time implementations. | ||
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