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@InProceedings{ChagasWald:2016:ExDeMe,
               author = "Chagas, Ronan Arraes Jardim and Waldmann, Jacques",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)}",
                title = "Extrapolation of delayed measurements for fusion in a distributed 
                         sensor network",
            booktitle = "Proceedings...",
                 year = "2016",
                pages = "1319--1324",
         organization = "Mediterranean Conference on Control and Automation, 24. (MED)",
             keywords = "Embedded systems, Extrapolation, Kalman filters, Sensor networks 
                         Computational burden, Computational resources, Delayed 
                         measurements, Distributed networks, Distributed sensor networks, 
                         Memory requirements, Optimal estimates, Sub-optimal algorithms.",
             abstract = "The measurement extrapolation (ME) algorithm was devised to fuse 
                         delayed measurements in the Kalman filter. It is a suboptimal 
                         algorithm that greatly reduces the computational burden of the 
                         optimal Reiterated Kalman Filter (RKF). ME can be used in embedded 
                         systems that lack the required computational resources to compute 
                         the optimal estimate. However, it has not been extended yet to be 
                         applied in a distributed sensor network. Furthermore, it is 
                         verified here that the original ME algorithm provides a biased 
                         estimate, which can degrade the estimation accuracy. Thus, this 
                         work proposes to extend ME to fuse delayed measurements received 
                         by nodes in a distributed network, and to remove the bias using 
                         Bayesian concepts, improving the accuracy of the novel method. The 
                         ME computational burden and memory needs are theoretically 
                         analyzed and compared to those of the RKF. Finally, simulations of 
                         a simplified distributed network are presented to measure the 
                         performance of the new algorithm with respect to RKF and to 
                         validate the theoretical analysis. The results show that ME can 
                         provide an estimate with acceptable accuracy whereas the 
                         computational burden is greatly decreased and the memory 
                         requirements are only slightly increased compared to RKF.",
  conference-location = "Athens, Greece",
      conference-year = "21-24 June",
                  doi = "10.1109/MED.2016.7535932",
                  url = "http://dx.doi.org/10.1109/MED.2016.7535932",
                 isbn = "978-146738345-5",
             language = "en",
           targetfile = "chagas_extrapolation.pdf",
        urlaccessdate = "24 nov. 2020"
}


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