Gain fusion algorithm for decentralised parallel Kalman filters

作     者：Paik, BS Oh, JH 

作者机构：Korea Adv Inst Sci & Technol Dept Mech Engn Yusong Gu Taejon 305701 South Korea 

出 版 物：《IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS》 (IEE Proc Control Theory Appl)

年 卷 期：2000年第147卷第1期

页      面：97-103页

核心收录：

主　　题：Kalman filters filtering theory reduced order filter suboptimal decentralised algorithm multisensor systems Kalman filter decentralised sensor systems parallel algorithms gain fusion algorithm Sensor fusion Filtering methods in signal processing Parallel programming and algorithm theory Signal processing theory inverse covariances sensor fusion 

摘      要：A new gain fusion algorithm is proposed for application to decentralised sensor systems. The proposed algorithm gives computer-efficient suboptimal estimation results, such that it reconstructs the global estimate and covariance from local Kalman filter gains and estimates without significant loss of accuracy. Compared to the conventional algorithm, the smaller communication requirement and the removal of the calculation requirement of inverse covariances make the proposed algorithm more suitable for real time applications. A numerical example shows that the proposed algorithm provides a convincing suboptimal decentralised algorithm. In addition, the proposed gain fusion algorithm can be easily extended to accommodate local Kalman filters with reduced order.