摘要
本文基于随机有限集的高斯混合多目标滤波器(Gaussian Mixture Multi-Target Filter,GM-MTF)提出几种传感器控制策略.首先,基于容积卡尔曼高斯混合多目标非线性滤波器,借助两个高斯分布之间的巴氏距离,推导GM-MTF的整体信息增益,并以此为基础提出相应的传感器控制策略.另外,设计高斯粒子的联合采样方法对多目标滤波器的预测高斯分量进行采样,用一组带权值的粒子去近似多目标统计特性,利用理想量测集对粒子的权值进行更新,继而研究利用Rényi散度作为评价函数,提出一种适应性更好的传感器控制策略.最后,给出基于目标势的后验期望(Posterior Expected Number of Targets,PENT)评价的高斯混合实现过程.仿真实验验证了提出算法的有效性.
This paper proposes several sensor control strategies via Gaussian mixture multi-target filter(GM-MTF) with random finite set.First,on the basis of the cubature Kalman Gaussian mixture multi-target nonlinear filter,the global information gain of the GM-MTF is deduced through the Bhattacharyya distance between the two Gaussian distributions.Then,taking advantage of this information distance,this paper proposes a corresponding sensor control strategy.Furthermore,a joint sampling method of Gaussian particle is designed to sample the predicted Gaussian component of multi-target filter.Subsequently,a set of weighted particles are used to approximate the multi-target statistical characteristic,and their weights are updated with the ideal measurement set.Next,a Rényi divergence based sensor control strategy which has better adaptability is proposed.Finally,a detailed Gaussian mixture implementation of the posterior expected number of targets(PENT) is given.Simulation results verify the effectiveness of these algorithms.
作者
陈辉
贺忠良
连峰
黎慧波
CHEN Hui;HE Zhong-liang;LIAN Feng;LI Hui-bo(School of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou,Gansu 730050,China;Institute of Integrated Automation,School of Electronic and Information Engineering,Xi’an Jiaotong University,Xi’an,Shaanxi 710049,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2019年第3期521-530,共10页
Acta Electronica Sinica
基金
国家自然科学基金项目(No.61370037
No.61873116)
甘肃省科技计划项目(No.18YF1GA065
No.18JR3RA137)
关键词
传感器控制
多目标跟踪
高斯混合
有限集统计
部分可观测马尔可夫决策过程
sensor control
multi-target tracking
Gaussian mixture
finite set statistics
partially observable Markov decision process