Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

Song I.Y.; Jeon M.; Shin V.

首页> 外文期刊>Signal Processing, IET >Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

【24h】

Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

机译：带有延迟的离散时间不确定系统的滑动窗口卡尔曼滤波高效多传感器融合

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

In this study, we provide two computationally effective multisensory fusion filtering algorithms for discrete-time linear uncertain systems with state and observation time-delays. The first algorithm is shaped by algebraic forms for multirate sensor systems, and then we propose a matrix form of filtering equations using block matrices. The second algorithm is based on exact cross-covariance matrix equations. These equations are useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Furthermore, our proposed filtering algorithms are based on the sliding window strategy in order to achieve high estimation accuracy and stability under parametric uncertainties. The authors demonstrate the low computational complexities of the proposed fusion filtering algorithms and how the proposed algorithms robust against dynamic model uncertainties comparing with Kalman filtering with time delays.

机译：在这项研究中，我们为具有状态和观测时滞的离散时间线性不确定系统提供了两种计算有效的多传感器融合滤波算法。对于多速率传感器系统，第一种算法由代数形式成形，然后提出使用块矩阵的滤波方程的矩阵形式。第二种算法基于精确的互协方差矩阵方程。这些方程式可用于计算矩阵权重，以在多维多传感器环境中进行融合估计。此外，我们提出的滤波算法基于滑动窗口策略，以在参数不确定性下实现较高的估计精度和稳定性。作者证明了所提出的融合滤波算法的低计算复杂度，以及与具有时滞的卡尔曼滤波相比，所提出的算法对动态模型不确定性的鲁棒性。

著录项

来源
《Signal Processing, IET》 |2012年第5期|p.446-455|共10页
作者
Song I.Y.; Jeon M.; Shin V.;
展开▼
作者单位

School of Information and Mechatronics, Gwangju Institute of Science and Technology, 1 Oryong-Dong, Buk-Gu, Gwangju 500-712, South Korea;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词

相似文献

外文文献
中文文献
专利

1. Robust Weighted Fusion Kalman Estimators for Networked Multisensor Mixed Uncertain Systems With Random One-Step Sensor Delays, Uncertain-Variance Multiplicative, and Additive White Noises [J] . Gao Yuan, Deng Zili IEEE sensors journal . 2019,第22期

机译：具有随机单步传感器延迟，不确定方差乘法和加性白噪声的网络多传感器混合不确定系统的鲁棒加权融合卡尔曼估计
2. Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances [J] . Wenjuan Qi, Peng Zhang, Zili Deng Signal processing . 2014,第juna期

机译：具有不确定噪声方差的多传感器时变系统的鲁棒加权融合卡尔曼滤波器
3. Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances [J] . Wen-Juan Qi, Peng Zhang, Zi-Li Deng Journal of applied mathematics . 2014,第Pta4期

机译：具有不确定噪声方差的多传感器系统的加权融合鲁棒稳态稳态卡尔曼滤波器
4. Distributed robust fusion Kalman filtering for uncertain stochastic systems with random delays and missing measurements [C] . Chen Bo, Yu Li, Zhang Wen-An, 8th Asian Control Conference, 2011 . 2011

机译：具有随机延迟和缺失量的不确定随机系统的分布式鲁棒融合卡尔曼滤波
5. An efficient extended Kalman filter algorithm and its application to systems having large delays in some measurements. [D] . Myers, Mary Annette. 1991

机译：一种有效的扩展卡尔曼滤波算法及其在某些测量中具有较大延迟的系统中的应用。
6. Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications [O] . Yingting Luo, Yunmin Zhu, Dandan Luo, 2008

机译：全局最优多传感器分布式随机参数矩阵卡尔曼滤波融合及其应用
7. Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances [O] . Wen-Juan Qi, Peng Zhang, Zi-Li Deng 2014

机译：具有不确定噪声差异的多传感器系统的加权融合稳健稳态卡尔曼滤波器

Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

摘要

著录项

相似文献

相关主题

期刊订阅