Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
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DOI：
10.1016/j.automatica.2012.03.027
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发表时间：
2012-09-01
期刊：
影响因子：
6.4
通讯作者：
Stergioulas, Lampros K.
中科院分区：
文献类型：
--
作者：
Hu, Jun;Wang, Zidong;Stergioulas, Lampros K.
本文研究了一类在有限时域内存在多次测量缺失的非线性系统的扩展卡尔曼滤波问题。系统模型中既包含确定性非线性，也包含随机性非线性，其中随机性非线性通过统计方法描述，能够反映乘性随机干扰。测量缺失现象随机发生，每个传感器的缺失概率由一个满足区间[0, 1]上某一概率分布的独立随机变量控制。这种概率分布允许是区间[0, 1]上任何常用的且条件概率已知的分布。所研究的滤波问题的目的是设计一个滤波器，使得在存在随机性非线性和多次测量缺失的情况下，滤波误差协方差存在一个上界。随后，通过在每个采样时刻合理设计滤波器增益使该上界最小化。结果表明，所需的滤波器可以通过两个类似黎卡提差分方程的解得到，这两个方程的形式适合在线应用中的递归计算。给出了一个示例来说明所提出的滤波器设计方案的有效性。© 2012爱思唯尔有限公司。保留所有权利。
In this paper, the extended Kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon. Both deterministic and stochastic nonlinearities are included in the system model, where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances. The phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval [0, 1]. Such a probability distribution is allowed to be any commonly used distribution over the interval [0, 1] with known conditional probability. The aim of the addressed filtering problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the filtering error covariance. Subsequently, such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati-like difference equations that are of a form suitable for recursive computation in online applications. An illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme. (C) 2012 Elsevier Ltd. All rights reserved.