Cooperative Tracking Using Vision Measurements on SeaScan UAVs

Abstract

A cooperative tracking approach for uninhabited aerial vehicles (UAVs) with camera-based sensors is developed and verified with flight data. The approach utilizes a square root sigma point information filter, which takes important properties for numerical accuracy (square root), tracking accuracy (sigma points), and fusion ability (information). Important augmentations to the filter are also developed for delayed data, by estimating the correlated processes, and moving targets, by using multiple models in a square root interacting multiple model formulation. The final form of the algorithm is general and scales well to any tracking problem with multiple, moving sensors. Flight data using the SeaScan UAV is used to verify the algorithms for stationary and moving targets. Cooperative tracking results are evaluated using multiple test flights, showing excellent results.