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A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data

机译：SMC采样器，用于根据噪声数据进行联合跟踪和目的地估计

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In this paper we describe a Sequential Monte Carlo (SMC) sampler that performs joint tracking and destination estimation of a target traveling along a known road network, as its journey progresses. The destination estimation is based on a simplistic model of driver intent, which assumes no prior knowledge of the history of visited destinations. The proposed algorithm is capable of refining the distribution of destinations that can be inferred from an incoming stream of position estimates. We compare the performance achieved by the proposed algorithm with a mainstay Particle Filter, demonstrating how the later suffers greatly from sample impoverishment, therefore necessitating an ever increasing number of particles as the number of possible destinations increases, while showcasing that the issue is significantly mitigated by the proposed SMC Sampler.
机译：在本文中，我们描述了一种顺序蒙特卡洛（SMC）采样器，该采样器对随着已知道路网络行进的目标进行联合跟踪和目的地估计。目的地估计基于驾驶员意图的简单模型，该模型不假设已探访目的地历史的先验知识。所提出的算法能够完善可以从位置估计的传入流中推断出的目的地的分布。我们将提出的算法与主流粒子滤波器进行比较，展示了后者如何遭受样本贫困的严重影响，因此随着可能的目标数量的增加，粒子的数量必将不断增加，同时证明该问题可以通过以下方法得到显着缓解：建议的SMC采样器。

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