| 基于加权最小二乘的卡尔曼滤波算法 |
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| 引用本文： | 陈鹏,钱徽,朱淼良.基于加权最小二乘的卡尔曼滤波算法[J].计算机科学,2009,36(11):230-231. |
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| 作者姓名： | 陈鹏 钱徽 朱淼良 |
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| 作者单位： | 1. 浙江大学计算机科学与技术学院,杭州,310027;安徽师范大学物理与电子信息学院,芜湖,241000
2. 浙江大学计算机科学与技术学院,杭州,310027 |
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| 基金项目： | 浙江省科技厅重大项目 |
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| 摘 要： | 为了将卡尔曼滤波(KF)应用于非线性系统中,利用了离散采样点将非线性模型线性化.通过加权最小二乘原理.得到近似的线性化模型,再将KF算法应用于这个线性模型中.结果表明,加权最小二乘与KF结合的方法在非线性模型中的计算结果同扩展卡尔曼滤波(EKF)算法接近,且不需要EKF那样求偏导就能很容易地应用到非线性系统中.这种方法实现容易,预测可靠,具有实际应用的价值.
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| 关 键 词： | 预测 非线性系统 卡而曼滤波 采样 |
| 收稿时间： | 2008/12/31 0:00:00 |
| 修稿时间： | 3/5/2009 12:00:00 AM |
Weighted Minimum Mean Square Kalman Filter |
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| CHEN Peng,QIAN Hui,ZHU Miao-liang.Weighted Minimum Mean Square Kalman Filter[J].Computer Science,2009,36(11):230-231. |
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| Authors: | CHEN Peng QIAN Hui ZHU Miao-liang |
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| Affiliation: | (College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China);(College of Physics and Electronic Information,Anhui Normal University,Wuhu 241000,China) |
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| Abstract: | In order to use Kalman Filter (KF) in nonlinear systems, a new method was proposed. Using the principle that a set of discretely sampled points can be used to form a linear system, the estimator yields performance ectuivalent to the Extended Kalman Filter (EKF) for nonlinear systems and can be elegantly used to nonlinear systems without the differential steps required by the EKF. We argue that the ease of implementation and more accurate estimation features of the new filter recommend its use in applications. |
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| Keywords: | Estimation Non-linear systems Kalman filtering Sampling |
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