Safe integration of Unmanned Aircraft Systems (UAS) into the civil airspace requires the development of a sense and avoid (SAA) capability that enables UAS to remain "well clear" from other airborne traffic. Providing this capability when encountering non-cooperative, maneuvering intruders, such as those operating under Visual Flight Rules (VFR), is particularly challenging due to the inherent uncertainties in predicting the future trajectories of these intruders. Experts have suggested [1] that one way of meeting this challenge is to treat "well clear" as a separation standard that is quantified using the risk (i.e. probability) of Near Mid-Air Collision (NMAC) at some future time, and to alert pilots when action is required to avoid violating this separation. This involves (explicitly or implicitly) a stochastic model to quantify likely intruder trajectories. In this paper, we develop algorithmic tools for computing such risk by expanding techniques developed in the target tracking community. A central feature of this approach is the use of continuous-time, maneuver-based (rather than traditional diffusion-based) stochastic models that are more representative of variations in maneuvering aircraft trajectories over longer time scales. We argue that evaluating risk using such models is computationally viable for a real-time SAA system and can provide enhanced performance in terms of the traditional detection-theoretic metrics of probability of detection (Pd) and probability of false alarm (Pfa).
Computing risk for Unmanned Aircraft self separation with maneuvering intruders
01.10.2012
1245664 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Computing risk for unmanned aircraft self separation with maneuvering intruders
IEEE | 2012
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