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Cooperative/Parallel Kalman Filtering for Decentralized Network Navigation
Wenyun GAO Xi CHEN Dexiu HU Haisheng XU
Publication
IEICE TRANSACTIONS on Communications
Vol.E99B
No.9
pp.20872098 Publication Date: 2016/09/01
Online ISSN: 17451345
DOI: 10.1587/transcom.2016EBP3006
Type of Manuscript: PAPER Category: Navigation, Guidance and Control Systems Keyword: network navigation, decentralized algorithm, cooperative algorithms, Kalmen filter,
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Summary:
This paper presents noniterative cooperative/parallel Kalman filtering algorithms for decentralized network navigation, in which mobile nodes cooperate in both spatial and temporal domains to infer their positions. We begin by presenting an augmented minimummeansquare error (MMSE) estimator for centralized navigation network, and then decouple it into a set of local subones each corresponding to a mobile node; all these subestimators work in parallel and cooperatively — with the state estimates exchanging between neighbors — to provide results similar to those obtained by the augmented one. After that, we employ the approximation methods that adopted in the conventional nonlinear Kalman filters to calculate the secondorder terms involved in these subestimators, and propose a decentralized cooperative/parallel Kalman filtering based network navigation framework. Finally, upon the framework, we present two cooperative/parallel Kalman filtering algorithms corresponding to the extended and unscented Kalman filters respectively, and compare them with conventional decentralized methods by simulations to show the superiority.

