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A next-generation aircraft collision avoidance system under development is designed to reduce unnecessary alerts while reducing the risk of mid-air collision. Current and future collision avoidance systems rely upon range, bearing and altitude information from airborne, beacon-based surveillance. Realistic models of surveillance error characteristics are needed in order to develop and certify the safety and performance of these systems. Prior models of bearing measurements assumed uncorrelated Gaussian noise. This study explains how hidden Markov models can be used to capture the time-correlated error characteristics exhibited by recorded surveillance data. Simulations show the impact of this higher-fidelity model on predicted collision avoidance performance.

No abstract