| 引用本文: | 李曦,杨乐,郭福成,等.高轨双星辐射源跟踪的高斯和-容积Kalman滤波算法.[J].国防科技大学学报,2016,38(2):99-106.[点击复制] |
| LI Xi,YANG Le,GUO Fucheng,et al.Gaussian-sum based cubature Kalman filtering algorithm for source geolocation using dual geostationary satellites[J].Journal of National University of Defense Technology,2016,38(2):99-106[点击复制] |
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| 高轨双星辐射源跟踪的高斯和-容积Kalman滤波算法 |
| 李曦1, 杨乐1,2, 郭福成2, 刘洋2, 张敏2 |
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(1.江南大学 物联网工程学院， 江苏 无锡 214122;2.国防科技大学 电子科学与工程学院， 湖南 长沙 410073)
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| 摘要: |
| 针对辐射源运动方程和观测方程的强非线性，提出基于高斯和框架与5阶容积Kalman滤波(5CKF)的跟踪算法GS-5CKF。该方法将起始时刻的时差观测量所确定的位于地球表面的时差线按经度等间隔划分，初始化多个并行的5CKF，线性组合各滤波器的输出获得辐射源运动状态的估计。针对5CKF，提出新的非线性测度并引入滤波器分裂与合并，从而提高了跟踪精度，同时保持GS-5CKF算法复杂度基本不变。仿真表明，相对仅使用单个5CKF和基于高斯和框架但使用3阶容积Kalman滤波器的GS-3CKF等方法，提出的算法具有更高的估计精度。 |
| 关键词: 高斯和 5阶容积Kalman滤波 辐射源跟踪 非线性滤波 分裂与合并 |
| DOI：10.11887/j.cn.201602017 |
| 投稿日期：2015-04-16 |
| 基金项目:国家自然科学基金青年科学基金资助项目（61304264，61305017）；江苏省自然科学基金资助项目（BK20140166） |
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| Gaussian-sum based cubature Kalman filtering algorithm for source geolocation using dual geostationary satellites |
| LI Xi1, YANG Le1,2, GUO Fucheng2, LIU Yang2, ZHANG Min2 |
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(1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;2. School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| To tackle the inherent high nonlinearity of motion equation and observation equation of radiation source, a GS (Gaussian-sum) based 5CKF (5th-order cubature Kalman filter) tracking algorithm, referred to as GS-5CKF, was proposed. It consists of multiple parallel 5CKFs, which were initialized through partitioning the candidate source positions determined by the time difference of arrival measurement at the beginning of the tracking process with respect to the source latitude. The linear combination of filter outputs was conducted to estimate the motion state of radiation source. A new nonlinearity measure was advocated, on the basis of which a filtering splitting and merging procedure was developed to further enhance the performance of GS-5CKF while keeping its computational complexity fixed. Simulation results show that: compared with the tracking algorithms using the single 5CKF and the GS-3CKF, the newly proposed GS-5CKF technique exhibits higher source geolocation accuracy. |
| Keywords: Gaussian sum 5th-order cubature Kalman filtering source tracking nonlinear filtering splitting and merging |
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