多传感器高斯混合PHD融合多目标跟踪方法  

Multi-sensor Gaussian Mixture PHD Fusion for Multi-target Tracking

作　　者：申屠晗[1] 薛安克[1] 周治利 

机构地区：[1]杭州电子科技大学自动化学院、信息科学与控制工程研究所,杭州310018

出　　处：《自动化学报》2017年第6期1028-1037,共10页Acta Automatica Sinica

基　　金：国家自然科学基金重大仪器专项项目(61427808);国家自然科学基金重点项目(61333009)资助

摘　　要：针对复杂环境下单传感器多目标跟踪方法效果不佳的问题,基于FISST(Finite set statistics)跟踪理论提出一种多传感器高斯混合PHD(Probability hypothesis density)多目标跟踪方法.首先,分析了FISST下多传感器PHD的形式化滤波器,在此基础上构建一种反馈式多传感器PHD融合跟踪框架;进一步利用高斯混合技术提出多传感器PHD跟踪方法;最后,通过解决多传感器后验PHD粒子匹配与融合问题提出三种算法.仿真实验表明,与常规高斯混合PHD跟踪算法相比,本文所提算法能够有效提高目标跟踪精度和鲁棒性.As the performance of single sensor multi-target tracking method will degenerate under complicated environment, a multi-sensor Gaussian mixture PHD multi-target tracker is proposed in terms of FISST theory. First, the formalized PHD filter is analyzed with FISST. Then, a multi-sensor posterior PHD feedback fusion framework is constructed. Further, Gaussian mixture technique is employed to build a multi-sensor PHD tracking method. At last,three applicable algorithms are proposed by solving particle matching and fusion problem. Simulation results show that,compared to some common Gaussian mixture PHD algorithms, the proposed algorithms are more accurate and robust.

关 键 词：多传感器多目标跟踪 有限集统计 概率假设密度 高斯混合 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]