An adaptive threshold for change detection methods using a windowed entropy-based criterion - Application to fault-tolerant fusion in collaborative mobile robotics

摘要

Nowadays, process supervision occupies an important place in quality control, cooperative localization in mobile robotics, video and image processing, and intelligent system design, to name a few. Indeed, any failure of such processes can reduce performance and have serious consequences. The development of statistical methods, capable of detecting and locating anomalies in these dynamic systems as quickly as possible, is of real interest. In this context, we have proposed in a previous study a reformulation of the change detection strategy using an entropy-based criterion. Our approach allowed the calculation of an adaptive threshold, unlike the Bayes criterion. In this paper, we propose an improvement of this study by introducing the use of an optimal window of observations. We validate the proposed approach to the Exponentially Weighted Moving Average (EWMA) control charts, which is a commonly used change detection technique. Our strategy is illustrated on a well-known example of the literature. Finally, this windowed entropy-based criterion allows one to design a fault-tolerant fusion methodology, which is experimentally validated from an extended Kalman filter (EKF) in collaborative mobile robotics.