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Aerial Vehicle Navigation with Terrestrial Signals of Opportunity: Performance Analysis and Transmitter Selection


The performance analysis and transmitter selection for an aerial vehicle navigating with terrestrial signals of opportunity (SOPs) is studied. The following problem is considered. An aerial vehicle is navigating in an environment where global navigation satellite system (GNSS) signals are unavailable. The aerial vehicle is assumed to be equipped with an onboard receiver capable of extracting psuedorange observations from an abundant number of terrestrial SOP towers. Each SOP tower contains dynamic, stochastic clock error states (bias and drift) which are estimated as the difference between the receiver's and each SOP's clock bias and clock drift terms. A dynamic estimator (e.g., an extended Kalman filter (EKF)) is employed to fuse the psuedorange observations to simultaneously localize the aerial vehicle and SOP towers. A lower bound on the error covariance of radio simultaneous localization and mapping (SLAM) with terrestrial SOPs is derived. In addition, it is shown that the so-called radio SLAM base case is observable, in which an aerial vehicle with imperfect knowledge about its initial states is navigating in an environment containing one unknown SOP tower and two partially known SOP towers (i.e., towers whose position are known, but clock error states are unknown). Furthermore, the computationally efficient transmitter selection strategies, termed opportunistic greedy selection (OGS) and one shot selection (OSS), for selecting the most informative terrestrial SOPs subset is developed. These transmitter selection strategies will exploit the additive, iterative properties of the Fisher Information Matrix (FIM) to minimize the aerial vehicle's average position error variance (i.e., A-optimality criterion). Simulation results demonstrate the derived lower bound on the error covariance numerically via Monte Carlo (MC) runs and analyzes the performance of different transmitter selection strategies. Experimental results are presented in two different scenarios: (i) unmanned aerial vehicle (UAV) with an initial estimate of its position making pseudorange observations on two partially known and one unknown cellular SOP, and (ii) U.S. Air Force high altitude aircraft navigating with pseudorange observations from terrestrial SOPs in a rural environment tasked with selecting K = 15 out of M = 57 total SOPs and in a semi-urban environment tasked with selecting K = 9 out of M = 18 total SOPs.

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