Dual-rate sampled-data systems. Some interesting consequences from its frequency response analysis

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of General Systems on JUL 4 2019, available online: http://www.tandfonline.com/10.1080/03081079.2019.1608984[EN] The main goal of this contribution is to introduce a new procedure in order to analyse properly SISO dual-rate systems (DRS) and to provide straightforward answers to some common general questions about this kind of systems. Frequency response analysis based on DRS lifting modelling can lead to interesting results about stability margins or performance prediction. As a novelty, it is explained how to understand DRS frequency response and how to handle it for an easy computation of magnitude and phase margins keeping classical frequency domain methods. There are also some repetitive questions about DRS that can be analysed and answered properly using the results from this contribution: what the optimum relation between sampling periods is or what effects does delay have in a DRS. Every step is illustrated with examples that should clarify the understanding of the text.Salt Llobregat, JJ.; Alcaina-Acosta, JJ. (2019). Dual-rate sampled-data systems. Some interesting consequences from its frequency response analysis. International Journal of General Systems. 48(5):554-574. https://doi.org/10.1080/03081079.2019.1608984S55457448

Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal

This paper focuses on the least-squares linear fusion filter design for discrete-time stochastic signals from multisensor measurements perturbed not only by additive noise, but also by different uncertainties that can be comprehensively modeled by random parameter matrices. The additive noises from the different sensors are assumed to be cross-correlated at the same time step and correlated with the signal at the same and subsequent time steps. A covariancebased approach is used to derive easily implementable recursive filtering algorithms under the centralized, distributed and sequential fusion architectures. Although centralized and sequential estimators both have the same accuracy, the evaluation of their computational complexity reveals that the sequential filter can provide a significant reduction of computational cost over the centralized one. The accuracy of the proposed fusion filters is explored by a simulation example, where observation matrices with random parameters are used to describe different kinds of sensor uncertainties.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER [grant number MTM2017- 84199-P]