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

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 Cramér-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 Cramér-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.