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机构地区:[1]江南大学物联网工程学院,江苏无锡214122 [2]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122 [3]国防科学技术大学电子科学与工程学院,湖南长沙410073
出 处:《计算机与现代化》2017年第10期87-94,共8页Computer and Modernization
基 金:国家自然科学基金青年基金资助项目(61304264;61305017);江苏省"六大人才高峰"第十一批高层次人才项目(DZXX-026);2016年度"江苏省博士后科研资助计划"项目(1601012A);中央高校基本科研业务费专项资金资助项目(JUSRP1509XNG)
摘 要:针对基于高轨双星时频差观测的无源跟踪问题,提出一种适用于跟踪高超音速巡航目标GMM-AEKF算法,该算法使用基于Euler采样建立的WGS-84椭球下的目标离散时间运动方程,在已有的GMM-EKF跟踪框架的基础上引入时差均匀高斯混合(GMM)表示、替代扩展卡尔曼滤波(AEKF)和基于Kullback-Leibler散度的高斯分量管理。仿真实验结果表明,AEKF的引入使得跟踪算法的状态更新运算变为线性,其估计精度收敛速度较快,适用于高超音速目标跟踪。This paper considers tracking a cruising hypersonic object with known altitude using the time difference of arrival( TDOA) and frequency difference of arrival( FDOA) measurements obtained at two geosynchronous satellites. A new tracking algorithm,referred to as GMM-AEKF,is proposed. The algorithm utilizes a discrete-time process equation under the WGS-84 ellipsoidal Earth model,which is established through discretizing the continuous-time equation of target motion with Euler sampling. GMM-AEKF generalizes the existing GMM-EKF algorithm in the sense that it implicitly takes into account the object moving along the earth surface with known altitude. It also includes a new method that can yield a more uniform GMM representation of the TDOA measurement,the alternative extended Kalman filter( AEKF) for FDOA track update,and a Kullback-Leibler( KL) divergence-based Gaussian component management scheme. Simulation results reveal that with the use of AEKF,TDOA and FDOA track updates of GMM-AEKF are the same as the state update of standard linear Kalman filter( KF). GMM-AEKF is also shown to be able to converge faster,which makes it more suitable for hypersonic object tracking other state-of-art benchmarks.
关 键 词:高斯和滤波 替代扩展卡尔曼滤波 时频差 欧拉采样 Kullback-Leibler散度
分 类 号:TN971[电子电信—信号与信息处理]
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