Robust Distributed Estimation Using Low-Complexity Approximations to the Kalman Filter

Abstract

This project aims to develop innovative solutions to improve access to the mobile Internet of the future and 6G wireless communications networks, the training of human resources in the area of telecommunications with knowledge of wireless Internet access and centralized and decentralized employment of clouds, the dissemination of knowledge through high-impact specialized publications, the study of new applications and the transfer of technology between universities, research institutes and the telecommunications industry.The goal of this post-doctoral reasearch plan is to extend our previous results and develop low-cost approximations to the Kalman filter that are robust against uncertainties in the model for the variation of the parameter vector to be estimated, comparing the results to more traditional approaches in the literature. Due to the low complexity, this kind of algorithm would allow the implementation of algorithms with better tracking properties in sensor networks, and the robustness would allow the application to a class of problems in which performance guarantees are important, such as distributed control (cooperative autonomous systems, for example).