@InProceedings{7362431,
  author = {N. Chopin and M. Gerber},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Application of sequential Quasi-Monte Carlo to autonomous positioning},
  year = {2015},
  pages = {489-493},
  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/√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 [1], which converges at a faster rate, and we illustrate the greater performance of SQMC on autonomous positioning problems.},
  keywords = {Monte Carlo methods;signal processing;state-space methods;sequential Quasi-Monte Carlo;autonomous positioning;particle filters;state-space models;low-discrepancy point sets;autonomous positioning problems;signal processing;Yttrium;Signal processing algorithms;Monte Carlo methods;Vehicles;Signal processing;Europe;Approximation algorithms;Low-discrepancy point sets;Particle filtering;Quasi-Monte Carlo},
  doi = {10.1109/EUSIPCO.2015.7362431},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104259.pdf},
}

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