Real-time Trajectory Optimization for Collaborative Self-Localization in Random Aircraft Formations

Jonathon S. Gipson, Christine M. Schubert Kabban, Robert C. Leishman, Juan D. Jurado

Abstract:	In network localization scenarios, the geometry between the anchor nodes and the node of interest determines the minimum variance of the relative position error, commonly known as the Cramer-Rao Lower Bound. With modern ´ computing resources, methods for real-time trajectory optimization are possible. The primary contribution of this paper is a distributed method for real-time self-localization trajectory optimization in a wireless sensor network of random formation aircraft. To perform this task, this paper introduces Self-Aligning Swarm, a distributed heuristic algorithm which generates a real-time trajectory for optimized self-localization based on local minimization of the trace of the Cramer-Rao ´ Lower Bound. Using a 10K-trial Monte Carlo simulation, the proposed algorithm is shown to improve 2D position MSE of the optimized aircraft, reduce MSE variance, and demonstrate the MSE distribution approaches normality.
Published in:	2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 20 - 23, 2020 Hilton Portland Downtown Portland, Oregon
Pages:	118 - 124
Cite this article:	Gipson, Jonathon S., Kabban, Christine M. Schubert, Leishman, Robert C., Jurado, Juan D., "Real-time Trajectory Optimization for Collaborative Self-Localization in Random Aircraft Formations," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 118-124. https://doi.org/10.1109/PLANS46316.2020.9109896
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