高斯混合多伯努利滤波器基于柯西施瓦兹散度的传感器控制方法

doi:10.3969/j.issn.0372-2112.2020.04.012

电子学报 ›› 2020, Vol. 48 ›› Issue (4): 706-716.DOI: 10.3969/j.issn.0372-2112.2020.04.012

高斯混合多伯努利滤波器基于柯西施瓦兹散度的传感器控制方法

陈辉, 贺忠良, 邓东明, 李国财

兰州理工大学电气工程与信息工程学院, 甘肃兰州 730050

收稿日期:2018-07-30 修回日期:2019-04-26 出版日期:2020-04-25 发布日期:2020-04-25
通讯作者: 陈辉
作者简介:贺忠良 男,1993年8月出生,河北临漳县人,硕士研究生.主要研究方向为多目标跟踪中的传感器管理.E-mail:zlhe93@163.com;邓东明 男,1989年10月出生,甘肃天水人,硕士研究生.主要研究方向为多目标跟踪中的传感器管理.E-mail:dengdongming19@163.com;李国财 男,1993年5月出生,甘肃临洮县人,硕士研究生.主要研究方向为多扩展目标跟踪.E-mail:liguocai93@163.com
基金资助:
国防基础科研项目（No.JCKY2018427C002）；国家自然科学基金（No.61873116，No.51668039，No.61763029）；甘肃省科技计划项目（No.18JR3RA137）

Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter

CHEN Hui, HE Zhong-liang, DENG Dong-ming, LI Guo-cai

School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China

Received:2018-07-30 Revised:2019-04-26 Online:2020-04-25 Published:2020-04-25

摘要/Abstract

摘要： 针对多目标跟踪中的传感器控制问题，本文基于有限集统计（FISST）理论，利用高斯混合多伯努利滤波器研究并提出相应的传感器控制策略.首先，文中给出容积卡尔曼高斯混合势均衡多目标多伯努利滤波器（CK-GMCBMeMBerF）的实现形式，并提取高斯混合分量近似多伯努利密度.然后，研究两个高斯混合之间的柯西施瓦兹（Cauchy-Schwarz）散度的求取，推导多目标概率密度变化所对应的信息增益，并以此为基础提出相应的传感器控制策略.此外，结合CK-GMCBMeMBer，详细推导了目标势的后验期望（PENT）准则的高斯混合（GM）实现过程，以GM-PENT作为评价准则进行相应的传感器控制方法的研究.最后，仿真实验验证了所提算法的有效性.

关键词: 多目标跟踪, 传感器控制, 有限集统计, 高斯混合, 信息增益

Abstract: In consideration of the sensor control for multi-target tracking,this paper proposes the corresponding sensor control strategy using Gaussian mixture multi-Bernoulli filter based on the FInite Set Statistics (FISST) theory.First,this paper gives the implementation of the Cubature Kalman Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter (CK-GMCBMeMBerF),and extracts the Gaussian mixture component to approximate multi-Bernoulli density.In addition,we study the solution of the Cauchy-Schwarz divergence between the two Gaussian mixture distributions,and derive the information gain corresponding to the change of multi-target probability density.Then,the corresponding sensor control strategy is proposed.Moreover,a detailed Gaussian Mixture (GM) implementation of the posterior expected number of targets (PENT) criteria is given based on CK-GMCBMeMBerF,and the corresponding sensor control strategy is studied with GM-PENT as the evaluation criteria.Finally,simulation results verify the effectiveness of these proposed algorithms.

Key words: multi-target tracking, sensor control, finite set statistics, Gaussian mixture, information gain

中图分类号:

TP274

陈辉, 贺忠良, 邓东明, 李国财. 高斯混合多伯努利滤波器基于柯西施瓦兹散度的传感器控制方法[J]. 电子学报, 2020, 48(4): 706-716.

CHEN Hui, HE Zhong-liang, DENG Dong-ming, LI Guo-cai. Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter[J]. Acta Electronica Sinica, 2020, 48(4): 706-716.

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高斯混合多伯努利滤波器基于柯西施瓦兹散度的传感器控制方法

Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter

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