TCAS Multiple Threat Encounter Analysis

This document is disseminated under the sponsorship of the Department of Transportation, Federal Aviation Administration, in the interest of information exchange. The United States Government assumes no liability for its contents or use thereof. The recent development of high-fi delity U.S. airspace encounter models at Lincoln Laboratory has motivated a simulation study of the Traffi c Alert and Collision Avoidance System (TCAS) multiple threat logic. We observed from archived radar data that while rarer than single-threat encounters, multiple threat encounters occur more frequently than originally expected. The multi-threat logic has not been analyzed in the past using encounter models. To generate multi-threat encounters, this report extends the statistical techniques used to develop pairwise correlated encounters. We generated and simulated a large number of multi-threat encounters using the TCAS logic implemented in Lincoln Laboratory's Collision Avoidance System Safety Assessment Tool. Near mid-air collision (NMAC) count indicates how often close encounters are resolved, unresolved, or induced by TCAS. Change in vertical miss distance shows the effect of the additional threat on the vertical separation between the fi rst two aircraft. Risk ratio measures how the probability of an NMAC changes when an aircraft is equipped with TCAS versus being unequipped. Study results indicate that in multi-threat encounters, the TCAS logic results in a more than twofold increase in unresolved NMACs and approximately fi ve times more induced NMACs than one-on-one encounters. TCAS provides a safety benefi t in multi-threat encounters by issuing resolution advisories that result in increased vertical separation between the equipped aircraft and the fi rst intruder. The recent development of high-fidelity U.S. airspace encounter models at Lin-coln Laboratory has motivated several follow-on efforts, one of which is a simulation study of the Traffic Alert and Collision Avoidance System (TCAS) multiple threat logic. Radar data from a collection of sensors throughout the U.S. were transferred to Lincoln Laboratory for the purpose of developing the encounter models. From these data, we observed that while rarer than single-threat encounters, multiple threat encounters occur more frequently than originally expected. The multi-threat logic has not been analyzed using an encounter model, and is not as well understood as the single-threat safety logic. To generate multi-threat encounters, this report extends the statistical techniques used to develop pairwise correlated encounters. We first use the correlated model to generate a one-on-one encounter, then use a modified version of the model to add an additional threat to the …