| 基于UKF的单站无源定位改进算法 |
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| 引用本文： | 黄耀光,李建新,高 博. 基于UKF的单站无源定位改进算法[J]. 现代雷达, 2012, 34(11): 45-48 |
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| 作者姓名： | 黄耀光 李建新 高 博 |
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| 作者单位： | 1. 中国人民解放军61906部队, 河北廊坊065001;解放军信息工程大学信息工程学院, 郑州450002
2. 解放军信息工程大学信息工程学院,郑州,450002 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 针对在单站无源定位中UKF算法由于数值计算的舍入误差带来的滤波发散以及计算量较大问题,文中提出了一种采用奇异值分解和超球体采样的UKF滤波算法。该算法将标准UKF算法中的协方差矩阵进行奇异值分解,避免算法在递推过程中,由于计算舍入误差而引起协方差矩阵失去正定性,而导致算法失效的问题。并且新算法采用超球体采样策略,减小了采样点的数量,提高了计算效率。仿真实验结果证明了该算法的有效性。
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| 关 键 词： | 单站无源定位 奇异值分解 超球体采样 无迹卡尔曼滤波 |
An Improved Single-Observer Passive Location Algorithm Based on UKF |
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| HUANG Yao-guang,LI Jian-xin and GAO Bo. An Improved Single-Observer Passive Location Algorithm Based on UKF[J]. Modern Radar, 2012, 34(11): 45-48 |
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| Authors: | HUANG Yao-guang LI Jian-xin GAO Bo |
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| Affiliation: | 1.Unit 61906 of PLA,Langfang 065001,China)(2.Institute of Information Engineering, Information Engineering University of PLA,Zhengzhou 450002,China) |
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| Abstract: | As the calculation of unscented Kalman filter is large and will divergent because of the numerical calculation error, animproved spherical simplex sampling UKF algorithm based on the singular value decomposition (SVD) is presented. To avoidingthe invalidation caused by errors during computation, the algorithm uses SVD technique to decompose the covariance matrix. Toimprove the computational efficiency, the algorithm uses spherical simplex sampling strategy to reduce the number of samplingpoints. Simulation results show that the validity of the proposed algorithm further. |
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| Keywords: | single-observer passive location singular value decomposition spherical simplex sampling unscented Kalman filter |
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