杂波环境下可移动主被动传感器长时调度方法  

Non-myopic scheduling method for mobile active/passive sensor in clutter environment

作　　者：安雷 李召瑞 吉兵 AN Lei;LI Zhaorui;JI Bing(Department of Electronic and Optical Engineering,Army Engineering University Shijiazhuang Campus,Shijiazhuang 050003,China)

机构地区：[1]陆军工程大学石家庄校区电子与光学工程系,河北石家庄050003

出　　处：《系统工程与电子技术》2023年第1期165-174,共10页Systems Engineering and Electronics

基　　金：国防预研项目(41404030101,41404030102)资助课题。

摘　　要：针对杂波环境下的多目标跟踪问题,基于可移动主被动传感器系统,提出了一种辐射控制的长时调度方法。首先,建立调度模型,对多目标运动状态和量测结果、传感器调度动作等进行数学描述;同时,基于雷达工作原理和截获概率的思想,提出改进的辐射风险量化方法。随后,利用高斯混合概率假设密度滤波算法预测长时跟踪精度,利用所提改进的量化方法预测长时辐射代价,并利用改进的灰狼优化算法求解传感器调度方案。最后,执行调度方案获得多目标量测信息,采用联合广义标签多伯努利滤波算法计算目标估计状态。仿真实验表明,所提调度方法在保证跟踪精度的基础上,能够实现对辐射代价的有效控制,与其他方法相比具有明显的优势。For multi-target tracking in clutter environment,a non-myopic scheduling method for radiation control is presented based on a mobile active/passive sensor system.Firstly,a scheduling model is established to describe the motion state and measurement results of multi-target,sensor scheduling actions and so on.At the same time,based on the principle of radar and the idea of interception probability,an improved radiation risk quantification method is proposed.Then,the Gaussian mixture probability hypothesis density filtering algorithm is used to predict the non-myopic tracking accuracy,the proposed improved quantization method is used to predict the non-myopic radiation cost,and the improved grey wolf optimization algorithm is used to solve the sensor scheduling scheme.Finally,the scheduling scheme is executed to obtain multi-target measurement information,and the joint generalized labeled multi-Bernoulli filtering algorithm is used to calculate the target estimation state.The simulation results indicate that the proposed scheduling method can effectively control the radiation cost while guaranteeing the tracking accuracy,and has obvious advantages over other methods.

关 键 词：传感器调度 多目标跟踪 随机有限集 辐射控制 灰狼优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN95[自动化与计算机技术—计算机科学与技术]