基于随机采样的多量测目标跟踪算法

刘伟峰; 柴中; 文成林

doi:10.3724/SP.J.1004.2013.00168

基于随机采样的多量测目标跟踪算法

doi: 10.3724/SP.J.1004.2013.00168

1.
杭州电子科技大学自动化学院系统科学与控制工程研究所杭州 310018

详细信息

通讯作者:
刘伟峰

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- 被引次数: 0
出版历程
- 收稿日期: 2011-10-26
- 修回日期: 2012-04-28
- 刊出日期: 2013-02-20

Multi-measurement Target Tracking by Using Random Sampling Approach

1.
Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018

摘要

摘要: 在多目标跟踪领域,传统算法假设目标是点源辐射体,至多产生一个量测点,随着现代传感器技术的发展,可以获得一个目标的多个量测. 本文研究当目标具有一定刚体几何形状并产生多量测的问题,这类目标称为多量测目标.首先,通过建立目标形状的刚体参数模型,提出采用参数马尔科夫链采样的方法,估计目标的形状参数.其次,采用等效量测方法,获得目标形心点的运动状态. 针对目标个数未知情况,在形状目标量测满足泊松分布假设条件下,采用泊松强度比方法获得目标的个数估计. 本文定义了目标类型概率并给出了目标类型概率的递推算法. 最后,通过三个具有不同形状和分布的多量测目标在二维平面的匀速(Constant velocity, CV)运动进行验证说明,实验表明: 所给方法在目标运动状态估计方面能够获得比较高的估计精度,目标形状估计能够比较稳定精确地估计目标形状的变化. 此外, 500次蒙特卡洛(Monte Carlo, MC)仿真实验表明,多量测目标的跟踪丢失率约为1.4%.
- 多量测目标跟踪 /
- 信息融合 /
- 形状参数 /
- 多量测 /
- 马尔科夫链采样
Abstract: In multi-target tracking field, conventional algorithms supposed that target is a point source and produces at most one measurement. While with the development of modern sensor technology, a target may give multiple measurements. In this paper, we consider that targets have certain geometrical shapes and give multiple measurements and call these targets multi-measurement targets (MMTS). We first build rigid models for the targets in parameter space and then estimate their parameters using the Markov chain sampling approach. Next, we derive the moving state described by target's centroid with our proposed equivalent measurement. When the number of targets remains unknown, under the Poisson assumption of number of target measurements, we use the ratios of Poisson intensities to estimate the number of targets. We also define the probabilistic vectors of type (PVOT) and propose a recursive process for the PVOT. To verify the proposed algorithm, the final experiment proposes three targets, with different shapes and distributions, moving in a 2-dimension plane with constant velocity (CV). The experimental results show that the estimation of target state has an excellent precision and the shape estimation can better and stably reflect the change of target shape. Besides, the target lost rate is around 1.4% in 500 Monte Carlo (MC) runs.
- Multi-measurement targets (MMTS) tracking /
- information fusion /
- shape parameter /
- multiple measurements /
- Markov chain (MC) sampling

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参考文献(1)

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