粒子滤波的路径采样及其在多目标跟踪中的应用
Path sampling for particle filters with application to multi-target tracking
AI总结：
提出一种基于路径采样的马尔可夫链蒙特卡洛步骤，附加到粒子滤波中，以将样本拉近观测值，显著提升多目标跟踪性能。
摘要：
在最近的工作（arXiv:1006.3100v1）中，我们提出了一种改进多目标跟踪粒子滤波的新方法。该方法基于随机微分方程的漂移同伦。漂移同伦被用于设计一个马尔可夫链蒙特卡洛步骤，该步骤附加到粒子滤波中，旨在使粒子滤波样本更接近观测值。在当前工作中，我们提出了一种替代方法，将马尔可夫链蒙特卡洛步骤附加到粒子滤波中，以使粒子滤波样本更接近观测值。当前和先前的方法都源于滤波问题的一般公式。我们已将当前提出的方法应用于线性和非线性观测模型的多目标跟踪问题。数值结果表明，所提出的方法可以显著提高粒子滤波的性能。
英文摘要：
In recent work (arXiv:1006.3100v1), we have presented a novel approach for improving particle filters for multi-target tracking. The suggested approach was based on drift homotopy for stochastic differential equations. Drift homotopy was used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the observations. In the current work, we present an alternative way to append a Markov Chain Monte Carlo step to a particle filter to bring the particle filter samples closer to the observations. Both current and previous approaches stem from the general formulation of the filtering problem. We have used the currently proposed approach on the problem of multi-target tracking for both linear and nonlinear observation models. The numerical results show that the suggested approach can improve significantly the performance of a particle filter.
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