Aerospace and Electronic Systems Magazine April 2018 - 37

ration. The scenario, if it evolves as shown in Figures 1, indicates
that by avoiding a collision with Aircraft 1, Aircraft 2 induces a
new potential secondary threat with Aircraft 3. The pilot of Aircraft
2 is expected to react within 2.5 seconds to the subsequent RA.
When the respective RA and all RAs are resolved, the TCAS in
each aircraft annunciates "Clear of Conflict".
Secondary encounter situations are very rare in current traffic densities (less than 0.3 aircraft per square nautical mile (NM)).
However, the likelihood of such situations will increase as density
increases. Thus, it is of great importance to analyze correlative influencing factors (e.g., pilot response, wind, flight density, and so
on) to improve the TCAS performance in heavy traffic scenarios to
assure safe and efficient operations. Since the mid-1980s, various
organizations and researchers have developed several encounter
models to support the development of the TCAS [7]. Williamson and Spencer [8] provided an overview of development of the
TCAS, including a basic description of the system operation, simulation results of operational evaluations conducted in cooperation

with the Piedmont, United, and Northwest Airlines, and an outline
of the status and progress of the TCAS implementation. Billingsley
et al. [9] designed an optimized collision avoidance problem as
a Markov decision process, which allowed the logic to be generated using a specified encounter model and performance metrics.
Netjasov et al. [10] proposed a model that contains the technical,
human, and procedural elements of TCAS operations and fully
supports mathematical analysis as well as rare event Monte Carlo
simulation of aircraft encounters. Tang et al. [11] specified the encounter model in colored Petri net (CPN) formalism to analyze
the congested traffic scenario in which the events that could transform a conflict into a collision were identified. Asmar et al. [12]
developed a modified Kalman filter that outperformed the general
Kalman filter in terms of tracking performance, and it improved
the operational capacity with negligible increase in computation.
Though aircraft mostly fly in preset trajectories, various
sources of uncertainty (especially the influence of wind) affect
the aircraft's ability to maintain their trajectories in a precise

Figure 1.

The scenario of three TCAS-equipped aircraft.

APRIL 2018

IEEE A&E SYSTEMS MAGAZINE

37



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