摘要
针对标准概率假设密度滤波器要求,新生目标强度作为先验信息需已知的工程限制,提出一种未知新生目标强度的多目标概率假设密度算法。该算法以概率假设密度滤波器为基础,通过充分利用目标运动信息及其与监视区域的相对关系来获取源于潜在新生目标的量测,并以此建模下一时刻滤波器所需的新生目标强度。仿真结果表明,所提算法在含有未知新生目标跟踪场景具有鲁棒的滤波性能,且其跟踪精度和计算代价均优于相关多目标PHD滤波器。
Aiming at the engineering constraint that the standard probability hypothesis density(PHD)filter requires a priori information of newborn target intensity,a multi-target probability hypothesis density algorithm for unknown strength of newborn target is proposed.Based on the PHD filter,the proposed algorithm obtains the measurements originated from potential newborn targets by making full use of target motion information and its relative relationship with the surveillance area,and the newborn target intensity for next time step required by the filter is modeled based on the obtained measurements.Simulation results demonstrate that the proposed algorithm has robust filtering performance in tracking scenes with unknown newborn targets,and its tracking accuracy and computational cost are better than the existing related multi-target PHD filters.
作者
高丽
张欢庆
GAO Li;ZHANG Huan-qing(Department of Mechanical and Electronic Engineering,Shangqiu Polytechnic,Shangqiu 476000,China;School of Electronic and Electrical Engineering,Shangqiu Normal University,Shangqiu 476000,China)
出处
《火力与指挥控制》
CSCD
北大核心
2020年第7期56-61,共6页
Fire Control & Command Control
基金
河南省科技攻关项目(182102210116)
河南省高等学校重点科研基金资助项目(19A520033)。
关键词
多目标跟踪
概率假设密度
高斯混合
未知新生目标
multi-target tracking
probability hypothesis density
gaussian mixture
unknown newborn targets
