@inproceedings{82bb7a3a26e84880ac1d05958e11a96e,
title = "Multi-level diffusion Kalman filter algorithm for adaptive networks",
abstract = "We study the problem of distributed state estimation over adaptive networks, where agents collaborate to estimate a common state parameter vector. If the sensing target area is too large or we want to improve the convergence speed of a large adaptive network, single-level diffusion algorithms do not have better performance, so we study the multi-level diffusion Kalman filter algorithm where a network running a diffusion strategy is enhanced by defining some special nodes called supernodes. In order to improve the estimation accuracy, we also study the weight-normalized diffusion Kalman filter algorithm. Simulation results show that multi-level diffusion Kalman filter algorithm has better accuracy and convergence performance than single-level diffusion Kalman filter algorithm. Furthermore, in order to further improve the algorithm's performance, we studied weight-normalized methods which are better than average weight methods.",
keywords = "Kalman filter, adaptive networks, diffusion strategy, distributed state estimation",
author = "Xiaorui Yang and Wenxuan Han and Lijuan Jia and Shunshoku Kanae",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8028772",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8895--8900",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "United States",
}