| 基于神经网络的无迹滤波改进算法 |
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| 引用本文： | 熊凯,张洪钺.基于神经网络的无迹滤波改进算法[J].航天控制,2005,23(4):18-23. |
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| 作者姓名： | 熊凯 张洪钺 |
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| 作者单位： | 北京航空航天大学自动化科学与电气工程学院,北京,100083 |
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| 基金项目： | 国家自然科学基金(60234010) |
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| 摘 要： | 介绍了采用无迹变换(UT)描述随机变量通过非线性系统后的均值及方差的方法,提出可以将神经模糊推理系统(ANFIS)用于确定无迹变换中的参数,使其对随机变量均值的描述达到二次以上精度,并给出了改进的无迹滤波器(UKF)结构和神经网络训练方法;仿真结果表明,该算法适用于系统含有未知输入或系统噪声为非高斯的情况,并可解决一些典型的非线性估计问题,改进算法的性能优于传统无迹滤波器。
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| 关 键 词： | 扩展卡尔曼滤波 无迹滤波 神经网络 |
| 文章编号： | 1006-3242(2005)04-0018-06 |
| 修稿时间： | 2004年11月29 |
The Improved Unscented Kalman Filter Based on Neural Network |
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| XIONG Kai,Zhang Hongyue.The Improved Unscented Kalman Filter Based on Neural Network[J].Aerospace Control,2005,23(4):18-23. |
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| Authors: | XIONG Kai Zhang Hongyue |
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| Institution: | Xiong Kai Zhang Hongyue School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083 |
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| Abstract: | The paper describes a method called unscented transformation (UT) for predicting mean and covariance in nonlinear system. A new approach to selecting sigma points by using adaptive neuro-fuzzy inference systems (ANFIS) is proposed and the sample points capture the posterior mean accurately to the 2rd or higher order for any nonlinearity. The superior performance of the improved unscented kalman filter (UKF) is clearly shown in a numerical example compared with the standard one. The new method has the great advantage of being able to handle unknown input and non-Gaussian noise. |
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| Keywords: | Extended Kalman filter Unscented Kalman filter Neural network |
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