Rogust Sensor Fusion Using Federated Kalman Filter and Discrete Generalized-Proportional-Integral Observers

Abstract

The federated Kalman filter has been an optimal solution when working with distributed systems providing a global estimation without affecting local filters. Several problems including nonlinearities and high-amplitude noise levels have been tackled to improve the performance of global estimations. In this work, we propose a robust federated Kalman filter composed of a set of discrete generalized-proportional-integral (GPI) observers. We demonstrate how this algorithm yields high-precision estimations by using sensor fusion and active disturbance rejection (ADR) features. The proposed method was compared with other state-of-the-art algorithms where ours had the best performance.