Probabilistic Estimation of State via Propagation of Error for a Collision Warning System

Abstract

The Federal Aviation Administration (FAA) is scheduled to open the national airspace to Unmanned Aerial Systems (UAS) by 2015. Although there are many beneficial applications by integrating UASs into the national airspace, the primary concern is UAS related midair collisions. The University of Washington Department of Aeronautics \& Astronautics Autonomous Flight System Laboratory (AFSL) is collaborating with Insitu, a UAS company, to develop a collision warning and awareness plugin (CAPlugin) for Insitu's Common Open-mission Management Command and Control (ICOMC2) system. The CAPlugin operates passively and notifies the operator when potential risks are detected at an increasing level of intensity. This paper's primary focus is the Forward State Estimator (FSE), a CAPlugin component, which predicts the vehicle's future position probability distribution, specifically for vehicles following a flight path. Error propagation is the methodology used by the FSE because minimal information is required for implementation. A point mass system involving a UAS following a flight path is the model used for deriving the error propagation in the vehicle's body frame. The paper present a scenario that demonstrates the CAPlugin capabilities and illustrates the FSE results, particularly for the flight path case.