《计算机应用研究》|Application Research of Computers

一种学生t混合粒子实现的概率假设密度滤波器

Student’s t particle implementations of probability hypothesis density filters

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作者 洪磊,陈树新,吴昊,徐涵,岳龙华
机构 1.空军工程大学 信息与导航学院,西安 710077;2.中国人民解放军93658部队,北京 100144
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文章编号 1001-3695(2020)06-009-1652-05
DOI 10.19734/j.issn.1001-3695.2018.11.0905
摘要 在非线性多目标跟踪问题中,高斯混合粒子概率假设密度(GMP-PHD) 滤波器在重尾的过程噪声和量测噪声的影响下会导致滤波性能的下降。针对该问题,提出一种新的学生t混合粒子概率假设密度(STMP-PHD)滤波器。该滤波器将过程噪声和量测噪声近似为学生t分布,并用学生t混合模型来近似多目标的强度;同时,利用蒙特卡罗方法计算学生t积分,建立了学生t混合形式的闭式递推框架。仿真结果表明,该滤波器能够有效克服由重尾的过程噪声和量测噪声带来的不利影响,并能够保持较高的跟踪精度。
关键词 多目标跟踪; 粒子滤波; 学生t分布; 非线性; 重尾噪声
基金项目 国家自然科学基金资助项目
本文URL http://www.arocmag.com/article/01-2020-06-009.html
英文标题 Student’s t particle implementations of probability hypothesis density filters
作者英文名 Hong Lei, Chen Shuxin, Wu Hao, Xu Han, Yue Longhua
机构英文名 1.Institute of Information & Navigation,Air Force Engineering University,Xi'an 710077,China;2.Unit 93658 of PLA,Beijing 100144,China
英文摘要 For the nonlinear multi-target tracking problem, the heavy-tailed process and measurement noises can reduce the performance of the Gaussian mixture particle probability hypothesis density(GMP-PHD) filter severely. To solve this problem, this paper proposed a new student's t mixture particle probability hypothesis density filter(STMP-PHD). The method used a student's t model to approximate the process noise and the measurement noise, and used a student's t mixture model to approximate the intensity of the multi-target. The algorithm made the Monte Carlo method to calculate the student's t integral, and establish the student's t mixture closed recursive framework. The simulation results confirmed that the filter can effectively overcome the negative effects of the heavy-tailed process noise and the measurement noise, and maintain the high tracking precision.
英文关键词 multi-target tracking; particle filter; student's t distribution; nonlinear; heavy-tailed noise
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页码 1652-1656
中图分类号 TN713
文献标志码 A