Swarm Control for Autonomous Navigation Support

In a denied environment, vehicles equipped with all-source navigation capabilities (e.g. magnetic, visual, etc.) can maintain assured PNT solutions and serve as anchors for other vehicles in a surveillance network. Mission vehicles not equipped with these features are subject to integration drift and can benefit from relative navigation updates. In a surveillance network, vehicles can perform relative measurement updates using radio ranging. In a time-synchronized network, localization accuracy for vehicles equipped only with radio ranging is primarily dependent on multilateration geometry to anchors. The primary contribution of this paper is a real-time distributed swarm control scheme to autonomously optimize multilateration for offboard mission vehicles in a surveillance network. Swarm guidance is provided by Semi-Cooperative Self-Aligning Swarm (SCSAS), a distributed greedy algorithm which uses surveillance data to control navigation support vehicles to optimize multilateration for selected offboard mission vehicles. Using multiple 10K-trial Monte Carlo simulations, the proposed algorithm demonstrates distributed control which results in consensus-like optimization of 2-D position MSE for selected offboard mission vehicles broadcasting surveillance data.