Collision avoidance system optimization for closely spaced parallel operations through surrogate modeling

Abstract

The Traffic Alert and Collision Avoidance System (TCAS) is mandated worldwide to protect against aircraft mid-air collisions. One drawback of the current TCAS design is limited support for certain closely spaced parallel runway operations. TCAS alerts too frequently, leading pilots to often inhibit Resolution Advisories during approach. Research is underway on the Airborne Collision Avoidance System X (ACAS X), a next-generation collision avoidance system that will support new surveillance systems and air traffic control procedures. ACAS X has been shown to outperform TCAS for enroute encounter scenarios. However, the design parameters that are tuned for the enroute environment are not appropriate for closely spaced parallel operations (CSPO). One concept to enhance the safety of CSPO is a procedure-specific mode of the logic that minimizes nuisance alerts while still providing collision protection. This thesis describes the application of surrogate modeling and automated search for the purpose of tuning ACAS X for parallel operations. The performance of the tuned system is assessed using a data-driven blunder model and an operational performance model. Although collision avoidance system development normally relies on human judgment and expertise to achieve ideal behavior, surrogate modeling is efficient and effective in tuning ACAS X for CSPO as the tuned logic outperforms TCAS in terms of both safety and operational suitability