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 /VN 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.
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