一种用于多目标跟踪的漂移同伦蒙特卡洛粒子滤波方法
A drift homotopy Monte Carlo approach to particle filtering for multi-target tracking
AI总结：
提出一种基于随机微分方程漂移同伦的马尔可夫链蒙特卡洛步骤，改进粒子滤波在多目标跟踪中的性能，并采用简单Metropolis算法解决目标-观测关联问题。
摘要：
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上，旨在使粒子滤波样本更接近观测值。此外，我们提出了一种简单的Metropolis蒙特卡洛算法来解决目标-观测关联问题。我们将所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：
We present a novel approach for improving particle filters for multi-target tracking. The suggested approach is based on drift homotopy for stochastic differential equations. Drift homotopy is 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. Also, we present a simple Metropolis Monte Carlo algorithm for tackling the target-observation association problem. We have used the 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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