Observability Driven Path Planing for Relative Navigation of Unmanned Aerial Systems
He Bai, Mechanical and Aerospace Engineering, Oklahoma State University; Clark N. Taylor, Sensors Directorate, Air Force Research Lab, Wright-Patterson
We consider path planning algorithms for relative navigation of two unmanned aerial systems (UAS) in GPS-denied environments. We design our algorithms to maximize state observability defined in discrete time. We consider two definitions of the nonlinear observability matrix and establish their connections with accumulated 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 system observability and sensitivity, we achieve significantly improved estimation performance over a predefined nominal trajectory for relative navigation.