A TCAS-II Resolution Advisory Detection Algorithm

Abstract

The Traffic Alert and Collision Avoidance System (TCAS) is a family of airborne systems designed to reduce the risk of mid-air collisions between aircraft. TCAS II, the current generation of TCAS devices, provides resolution advisories that direct pilots to maintain or increase vertical separation when aircraft distance and time parameters are beyond designed system thresholds. This paper presents a mathematical model of the TCAS II Resolution Advisory (RA) logic that assumes accurate aircraft state information. Based on this model, an algorithm for RA detection is also presented. This algorithm is analogous to a conflict detection algorithm, but instead of predicting loss of separation, it predicts resolution advisories. It has been formally verified that for a kinematic model of aircraft trajectories, this algorithm completely and correctly characterizes all encounter geometries between two aircraft that lead to a resolution advisory within a given lookahead time interval. The RA detection algorithm proposed in this paper is a fundamental component of a NASA sense and avoid concept for the integration of Unmanned Aircraft Systems in civil airspace.

From spatial conflict probes to spatial/temporal conflict probes: Why and how

“…the intruder has passed the CPA point), dmod is used as the spatial separation criterion. For larger values of ranges and range rates, the modified tau is nearly identical to true tau, but for smaller values it provides a more conservative threshold [13]. When closure rate approaches zero, the spatial threshold at which the alert is provided approaches DMOD.…”

“…In case of path II, the location where the TA will be triggered is at point 1 for a relative velocity of 250 m/s and point 2 for a relative velocity of 150 m/s. 13 In the actual TCAS algorithm, a modified tau is used. For a zero and negative closure rate (i.e.…”

Airborne Radar-Based Collision Detection and Risk Estimation for Small Unmanned Aircraft Systems

“…Aircraft trajectory predictions are based on linear projections of current aircraft states such that the uncertainty of the predicted trajectory is translated into areas around the predicted trajectory referred as to safe zones. Linear projections can be computationally efficient and prediction errors are negligible over short time horizons [36,37] or assumed known when flight plans are communicated [38]. On the other hand, the probabilistic methods estimate the probability of collision based on a probabilistic model of future intruder dynamics.…”

Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning

“…The CPA is firstly introduced in maritime domain for vessel CD problem [6,7]. Then, plenty of researches have been carried out on using CPA concept for 2D aircraft CD problem [2,[8][9][10][11][12][13][14]. CPA is also extended to be applied in 3D airspace [15,16].…”