首页> 外文期刊>Circuits and Systems I: Regular Papers, IEEE Transactions on >Distributed Federated Kalman Filter Fusion Over Multi-Sensor Unreliable Networked Systems
【24h】

Distributed Federated Kalman Filter Fusion Over Multi-Sensor Unreliable Networked Systems

机译:多传感器不可靠网络系统上的分布式联合卡尔曼滤波器融合

获取原文
获取原文并翻译 | 示例

摘要

This paper is concerned with the problem of distributed federated Kalman filter fusion (DFKFF) for a class of multi- sensor unreliable networked systems (MUNSs) with uncorrelated noises. An optimal DFKFF algorithm of MUNSs without buffer is presented, and rigorously proved to be equivalent to centralized optimal Kalman filter fusion (COKFF) algorithm of MUNSs without buffer. Finite length buffers deal with measurement delay or loss, and a suboptimal DFKFF algorithm of MUNSs with finite length buffers is proposed based on the optimal local Kalman filter with a buffer of finite length for each subsystem. Compared with COKFF algorithm of MUNSs with buffers, the proposed DFKFF algorithm of MUNSs with buffers has stronger fault-tolerance ability. Two simulation examples are given to illustrate the effectiveness of the proposed approaches.
机译:本文涉及一类具有不相关噪声的多传感器不可靠网络系统(MUNS)的分布式联合卡尔曼滤波融合(DFKFF)问题。提出了一种无缓冲的MUNS的最优DFKFF算法,并被严格证明等效于无缓冲的MUNS的集中式最优卡尔曼滤波融合(COKFF)算法。有限长度缓冲器处理测量延迟或损耗,并基于每个子系统具有有限长度缓冲器的最优局部卡尔曼滤波器,提出了具有有限长度缓冲器的MUNS的次优DFKFF算法。与带缓冲的MUNS的COKFF算法相比,所提出的带缓冲的MUNS的DFKFF算法具有更强的容错能力。给出了两个仿真例子来说明所提出方法的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号