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@Article{ChagasWald:2016:ThAnMe,
               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 = "Theoretical analysis of the measurement transportation algorithm 
                         to fuse delayed data in distributed sensor networks",
              journal = "IEEE Transactions on Signal and Information Processing Over 
                         Networks",
                 year = "2016",
               volume = "2",
               number = "3",
                pages = "246--259",
                month = "Sept",
             keywords = "Measurement transportation (MT), delayed measurements, distributed 
                         estimation, sensor network, Kalman filtering.",
             abstract = "Distributed sensor networks are capable of robust dynamic system 
                         estimation. The shared information in the network can prevent 
                         significant degradation or the interruption of the estimation 
                         process when a particular network node fails. However, the 
                         estimation accuracy can be severely degraded if delayed 
                         information is navely fused. The classical algorithm to fuse 
                         delayed measurements in a distributed network is the reiterated 
                         Kalman filter (RKF), which provides the optimal estimate in linear 
                         and Gaussian systems. Nevertheless, this algorithm imposes a huge 
                         computational burden and requires considerable memory when the 
                         delay is large, thus precluding the use of RKF in embedded systems 
                         that lack the needed computational resources. Previously, we 
                         proposed a suboptimal algorithm called measurement transportation 
                         (MT) that greatly reduces both thememory requirement and 
                         computational burden and delivers accuracy comparable to that of 
                         the RKF in a simulated UAV network. However, MT was only tested 
                         with numerical simulations. Here, we extend the previous 
                         investigation with the detailed analysis of MT regarding its 
                         accuracy, memory necessity, and computational burden. Cases are 
                         shown when the analysis predicts that the accuracy delivered by MT 
                         is comparable to that of the RKF and the theoretical results are 
                         then validated with a simulated distributed sensor network.",
                  doi = "10.1109/TSIPN.2016.2580461",
                  url = "http://dx.doi.org/10.1109/TSIPN.2016.2580461",
                 issn = "2373-776X",
             language = "en",
           targetfile = "chagas_theoretical.pdf",
        urlaccessdate = "28 nov. 2020"
}


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