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

作　　者：陈辉[1] 贺忠良 邓东明 李国财 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)

机构地区：[1]兰州理工大学电气工程与信息工程学院,甘肃兰州730050

出　　处：《电子学报》2020年第4期706-716,共11页Acta Electronica Sinica

基　　金：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)。

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.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.

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 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]