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未知噪声统计下多模型概率假设密度粒子滤波算法 被引量：5

Multiple-model probability hypothesis density filter for multi-target tracking without the statistics of noise parameters
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摘要 针对传统多目标概率假设密度滤波(PHD)器在噪声先验统计未知或不准确时滤波精度下降甚至丢失目标的问题,设计一种自适应多模型粒子PHD(MMPHD)滤波算法.该算法利用多模型近似思想,推导出一种多模型概率假设密度估计器,不仅能估计多目标状态,而且能实时估计未知且时变的噪声参数,并采用蒙特卡罗方法给出了MMPHD闭集解.仿真实例表明,所提出的算法具有应对噪声变化的自适应能力,可有效提高目标跟踪精度. When the prior noise statistic is unknown and time-varying, the conventional probability hypothesis density（PHD） filter declines in accuracy and loses targets. An adaptive multiple-model PHD（MMPHD） filter is proposed to estimate the states of targets and their noise variances. The unknown and time-varying noise is estimated based on multiple-models methods, and the MMPHD filter is designed to jointly estimate the target states and the statistics of the noise. The sequential Monte Carlo method is used to implement the MMPRD filter. Simulation results show that the proposed filter can accommodate the unknown measurement variances effectively and improve the estimation accuracy.
作者 吴鑫辉 黄高明 高俊
机构地区 海军工程大学电子工程学院
出处 《控制与决策》 EI CSCD 北大核心 2014年第3期475-480,共6页 Control and Decision
基金 国家863计划项目(2011AA7014061) 国家自然科学基金项目(60901069)
关键词 多目标跟踪 概率假设密度滤波器 多模型估计器 蒙特卡罗方法 multi-target tracking probability hypothesis density filter multiple-model estimator sequential Monte Carlo method
分类号 TP273 [自动化与计算机技术—检测技术与自动化装置]
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参考文献1

二级参考文献15

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共引文献11

同被引文献46

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控制与决策
2014年 第3期

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