一种改进的高斯逆威沙特概率假设密度扩展目标跟踪算法  

Improved Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking

作　　者：李文娟[1] 吕靖 顾红[1] 苏卫民[1] 马超[1] 杨建超[1] LI Wenjuan;Lü Jing;GU Hong;SU Weimin;MA Chao;YANG Jianchao(School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China)

机构地区：[1]南京理工大学电子工程与光电技术学院,南京210094

出　　处：《电子与信息学报》2018年第6期1279-1286,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61471198;61671246);江苏省自然科学基金(BK20160847;BK20170855)~~

摘　　要：假设扩展目标(ET)的扩展和量测数目分别为椭圆和泊松模型,高斯逆威沙特概率假设密度(GIW-PHD)能够估计扩展目标的运动和扩展状态。然而,该滤波器对空间邻近目标的数目、非椭圆目标和受到遮挡目标的扩展估计不够准确。针对这些问题,该文提出一种改进的GIW-PHD。首先,假设目标扩展为一个相同尺寸的参考椭圆,通过设计新的散射矩阵得到改进的随机矩阵(RM)方法。然后,将改进的RM方法与假设量测数目服从多伯努利分布的ET-PHD结合,得到改进的GIW-PHD滤波器。仿真和实验结果表明,与传统GIW-PHD相比,改进的GIW-PHD估计的目标数目和量测数目较多,扩展较大的椭圆和非椭圆目标的扩展更准确。Assumed that extension and measurement number of Extended Targets（ET） are respectively modeled as ellipse and Poisson,a Gaussian Inverse Wishart Probability Hypothesis Density（GIW-PHD） filter can estimate kinematic and extension states.However,for the number of spatially close targets and the extensions of non-ellipsoidal and occluded targets,the results estimated by this filter are not accurate enough.In view of these problems,an improved GIW-PHD filter is proposed in this paper.Firstly,assumed that target extension is modeled as a reference ellipse of the same size,a modified Random Matrix（RM） method is obtained by devising a new scatter matrix.Then,combining the improved RM method with the ET-PHD based on a measurement number multi-Bernoulli model,the improved GIW-PHD filter is obtained.Simulated and experimental results show that,compared with the traditional GIW-PHD,the improved GIW-PHD filter can obtain more accurate estimates in target number and the extensions of ellipsoidal and non-ellipsoidal targets with large measurement number and extensions.

关 键 词：扩展目标跟踪 高斯逆威沙特概率假设密度 随机矩阵 多伯努利分布 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]