跟踪机动目标的有效粒子滤波

T. Sathyan, M. Hedley
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引用次数: 6

摘要

准确跟踪精英运动员的表现监测,使运动科学家优化训练,以获得竞争优势。该应用面临的一个重要挑战是运动员的可操作性高,传统的卡尔曼滤波(KF)不能提供令人满意的跟踪精度。此外,通常需要每个玩家每秒数十次更新的高更新率,因此,所考虑的跟踪算法应该具有计算效率。本文提出了一种计算效率高的多模型粒子滤波(MM-PF)算法。它采用基于无气味KF的高斯建议密度和确定性采样技术,跟踪精度与增强的MM-PF相似,但计算成本低得多。通过仿真和田间试验数据验证了该算法的有效性。试验是与澳大利亚体育研究所合作进行的,使用的是我们设计的本地化系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient particle filtering for tracking maneuvering objects
Accurate tracking of elite athletes for performance monitoring allows sports scientists to optimize training to gain a competitive edge. An important challenge in this application is that the maneuverability of the athletes is high and the traditional Kalman filter (KF) will not provide satisfactory tracking accuracy. Further, high update rates, of the order of tens of updates per second for each player, are often required and hence, the tracking algorithm considered should be computationally efficient. In this paper we propose a computationally efficient multiple model particle filter (MM-PF) algorithm for tracking maneuvering objects. It uses a Gaussian proposal density based on the unscented KF and a deterministic sampling technique and provides tracking accuracy similar to that of the augmented MM-PF, but with much lower computational cost. The performance of the proposed algorithm was verified using simulations and data collected in field trials. The trials were conducted with the Australian Institute of Sport using a localization system we have designed.
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