欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
大多数传统的跟踪门技术仅使用目标的运动学量测信息,在多目标、多杂波跟踪场景中会导致较大的关联不确定性。考虑到属性传感器可以获取目标的类型信息,提出了基于目标联合状态类型概率密度的跟踪门方法。首先给出目标状态与类型的联合概率密度表示,从而导出以类为条件的跟踪门构建方法。为了适用于实时的非线性跟踪系统,门限的计算采用了基于仿真的算法。场景1显示如果目标的量测预测密度为偏斜函数时,基于仿真的门限算法可以获得最优的跟踪门;场景2为地面编队目标的跟踪过程。与使用传统的跟踪门相比,以类为条件的跟踪门技术在很大程度上提高了目标量测到航迹的关联率。
英文摘要：
Most conventional tracking gate techniques only use the targets＇ kinematic measurement information,which typically results in great uncertainties of measurement-to-track association for multi-target tracking in clutter.Considering that the target class information can be derived from attribute sensors,the tracking gate technique for joint target state-class probability density is proposed.Firstly,a joint probability density description of the target state and target class is given,by which the method for constructing the class-conditioned gates is developed.In order to comply with nonlinearity in practical application,evaluating of the gate threshold adopted an algorithm based on simulation.Scenario 1 shows that if the target predictive measurement density is skewed distribution,the simulation-based threshold-evaluating algorithm can achieve optimal gate volume;and scenario 2 presents a target tracking process for ground formation.Compared with the data association methods using traditional tracking gates,the class-conditioned gate technique significantly improves the probabilities of the measurement-to-track association.
同期刊论文项目
同项目期刊论文
期刊信息
位置：