| 基于RBF神经网络辅助的自适应UKF算法 |
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| 引用本文： | 郭文强,秦志光. 基于RBF神经网络辅助的自适应UKF算法[J]. 计算机应用, 2009, 29(3) |
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| 作者姓名： | 郭文强 秦志光 |
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| 作者单位： | 电子科技大学,计算机科学与工程学院,成都,610054;新疆财经大学,计算机科学与工程学院,乌鲁木齐,830012;电子科技大学,计算机科学与工程学院,成都,610054 |
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| 基金项目： | 国家自然科学基金，国家高技术研究发展计划(863计划) |
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| 摘 要： | 卡尔曼滤波能在测量噪声干扰下对系统状态进行无偏估计.但无论是扩展卡尔曼滤波(EKF)算法,还是无轨迹卡尔曼滤波(UKF)算法,都无法避免滤波发散现象.给出利用径向基函数(RBF)神经网络的自适应调整能力来对卡尔曼滤波输出进行校正,从而避免输出发散的算法.计算机模拟和实际应用表明,基于RBFNN的卡尔曼滤波算法可以有效防止输出发散.
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| 关 键 词： | 扩展卡尔曼滤波 无轨迹卡尔曼滤波 径向基函数神经网络 |
Research on RBFNN-aided adaptive UKF algorithm |
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| GUO Wen-qiang,QIN Zhi-guang. Research on RBFNN-aided adaptive UKF algorithm[J]. Journal of Computer Applications, 2009, 29(3) |
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| Authors: | GUO Wen-qiang QIN Zhi-guang |
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| Affiliation: | 1.School of Computer Science and Engineering;University of Electronic Science and Technology of China;Chengdu Sichuan 610054;China;2.School of Computer Science and Engineering;Xinjiang University of Finance and Economics;Urumchi Xinjiang 830012;China |
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| Abstract: | It is well known that the Kalman filter can be adopted to make unbiased estimation for system state with the measurement of the noise interference.However,neither the EKF algorithm nor the UKF algorithm can avoid filtering divergence.In terms of the adaptability of RBF neural network(RBFNN),this paper proposed a RBFNN-based algorithm to correct the output of the Kalman filter and further to avoid the output divergence.Both the simulation and the application results show that the output divergence can be eff... |
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