首页> 外文会议>International Conference on Information Fusion >Distributed Estimation using Square Root Decompositions of Dependent Information
【24h】

Distributed Estimation using Square Root Decompositions of Dependent Information

机译:使用依赖信息的Square Root分解分布估计

获取原文

摘要

Sensor networks allow robust and precise estimation by fusing estimates from several distributed sensor nodes. Because of the often limited communication resources, a trade-off between the amount of information communicated and the quality of the fusion result has to be made. On the one hand, obtaining the optimal fusion result often needs an infeasible amount of additional information, but on the other hand, conservative methods usually lead to more pessimistic results in comparison. This paper proposes a square root decomposition of the incorporated noise terms to reconstruct the cross-covariance matrices between sensor nodes. To save communication bandwidth, a residual is defined that allows bounding of the cross-covariance matrix with a reduced number of noise terms. The consistency of the proposed method is demonstrated by two simulation examples featuring a linear and a nonlinear setup and is compared with other state-of -the-art fusion methods.
机译:传感器网络允许通过熔化来自几个分布式传感器节点的估计来稳健和精确的估计。由于通信资源经常有限,所传达的信息量与融合结果的质量之间的权衡。一方面,获得最佳融合结果通常需要一种不可行的额外信息量,但另一方面,保守方法通常导致更悲观的结果相比。本文提出了具有掺入噪声术语的平方根分解,以重建传感器节点之间的交叉协方差矩阵。为了节省通信带宽,定义残差,允许具有减少数量的噪声项的交叉协方差矩阵。所提出的方法的一致性由具有线性和非线性设置的两个模拟示例来证明,并与其他最先进的融合方法进行比较。

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