Abstract
SMC (Sequential Monte Carlo) algorithms (also known as particle filters) are popular
methods to approximate filtering (and related) distributions of state-space
models. However, they converge at the slow $1/\sqrt{N}$ rate, which may be an issue
in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential
Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed
by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater
performance of SQMC on autonomous positioning problems.
methods to approximate filtering (and related) distributions of state-space
models. However, they converge at the slow $1/\sqrt{N}$ rate, which may be an issue
in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential
Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed
by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater
performance of SQMC on autonomous positioning problems.
| Original language | English |
|---|---|
| Title of host publication | Signal Processing Conference (EUSIPCO) |
| Subtitle of host publication | 2015 23rd European |
| Publisher | European Association for Signal Processing (EURASIP) |
| ISBN (Electronic) | 978-0-9928-6263-3 |
| Publication status | Published - 2015 |
Keywords
- Low-discrepancy point sets
- Particle filtering
- Quasi-Monte Carlo