NDLI: GAS decentralized navigation filters in a continuous-discrete fixed topology framework

Content Provider	IEEE Xplore Digital Library
Author	Viegas, D. Batista, P. Oliveira, P. Silvestre, C.
Copyright Year	2013
Description	Author affiliation: Inst. for Syst. & Robot., Inst. Super. Tecnico, Lisbon, Portugal (Viegas, D.; Batista, P.; Oliveira, P.) \|\| Dept. of Electr. & Comput. Eng., Univ. of Macau, Macau, China (Silvestre, C.)
Abstract	This paper addresses the problem of state estimation in formations of autonomous vehicles. The approach considered here consists in the implementation of a local state observer in each vehicle relying only on locally available measurements and data communicated by neighboring agents, resulting in a decentralized state observer which features lower computational and communication loads than comparable centralized solutions. A method for computing observer gains which yield globally asymptotically stable error dynamics is presented for fixed topology formations, as well as an iterative algorithm for improving the decentralized estimator's performance when the measurements are corrupted by noise. The proposed framework is particularized to the practical case of a formation of Autonomous Underwater Vehicles (AUVs), and a continuous-discrete formulation is achieved for the local state observers, to take into account the difference in sampling rates between on-board instrumentation and positioning systems. To assess the performance of the solution, simulation results are presented and discussed for different formation topologies.
Starting Page	1286
Ending Page	1291
File Size	472037
Page Count	6
File Format	PDF
e-ISBN	9781479909971
DOI	10.1109/MED.2013.6608885
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-25
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation error Observers Position measurement Topology Noise measurement Vehicle dynamics Vehicles
Content Type	Text
Resource Type	Article

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