Gain fusion algorithm for decentralised parallel Kalman filters

Abstract

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.