序贯拟蒙特卡罗方法在自主定位中的应用
Application of Sequential Quasi-Monte Carlo to Autonomous Positioning
摘要：
序贯蒙特卡罗算法（也称为粒子滤波器）是用于近似状态空间模型的滤波（和相关）分布的流行方法。然而，它们以缓慢的$1/\sqrt{N}$速率收敛，这在实时数据密集型场景中可能是一个问题。我们简要介绍了SQMC（顺序准蒙特卡罗），这是基于Gerber和Chopin（2015）提出的低差异点集的SMC变体，它以更快的速度收敛，我们说明了SQMC在自主定位问题上的更高性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
