Title: Observability Driven Path Planing for Relative Navigation of Unmanned Aerial Systems
Author(s): He Bai, Clark N. Taylor
Published in: Proceedings of IEEE/ION PLANS 2018
April 23 - 26, 2018
Hyatt Regency Hotel
Monterey, CA
Pages: 793 - 800
Cite this article: Bai, He, Taylor, Clark N., "Observability Driven Path Planing for Relative Navigation of Unmanned Aerial Systems," Proceedings of IEEE/ION PLANS 2018, Monterey, CA, April 2018, pp. 793-800.
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Abstract: We consider the problem of relative navigation of two unmanned aerial systems (UAS) in GPS-denied environments. We design active path planning algorithms to maximize state observability defined in discrete time. We consider two definitions of the nonlinear observability matrix and establish their connections with Fisher information matrix and filtering Cramer-Rao lower bound, respectively. We also define a sensitivity function that correlates noise on control inputs to errors on the state estimate. We demonstrate using Monte Carlo simulations that by optimizing metrics from the state observability and sensitivity, we achieve significantly improved estimation performance over a nominal trajectory for relative navigation.