Extended Traffic Alert Information to Improve TCAS Performance by means of Causal Models

Abstract

Near-midair collisions (NMACs) between aircraft have long been a primary safety concern and have incessantly motivated the development of ingenious onboard collision avoidance (CA) systems to reduce collision risk. The Traffic Alert and Collision Avoidance System (TCAS) acts as a proverbially accepted last-resort means to resolve encounters, while it also has been proved to potentially induce a collision in the hectic and congested traffic. This paper aims to improve the TCAS collision avoidance performance by enriching traffic alert information, which strictly fits with present TCAS technological requirements and extends the threat detection considering induced collisions and probabilistic pilot response. The proposed model is specified in coloured Petri net (CPN) formalism, to generate by simulation all the future possible downstream reachable states to enhance the follow-up decision making of pilots via synthesising relevant information related to collision states. With the complete state space, the potential collision scenarios can be identified together with those manoeuvres that may transform a conflict into a collision. The causal TCAS model is demonstrated to work effectively for complex multiaircraft scenarios and to identify the feasible manoeuvres that contribute to reduce the nonzero TCAS-induced collision risk.

A causal model to analye aircraft collision avoidance deadlock scenarios

“…Avoidance System (TCAS II v7.1), has been designed for operations in the traffic densities of 0.3 aircraft per squared nautical mile [1,8]. It demonstrates excellent performances for the pairwise encounters, as well as the great improvements for multi-thread encounters, taking different flight configurations (cruising and evolving aircraft) into considerations.…”

“…Continuous increase in the traffic density over the certain en-route sectors provokes many situations in which a loss of separation minima (SM) between two aircraft occurs [2,4]. Although, this loss is predicted well in advance, giving a proper look-ahead time (LAT) for a detection function, the resolution of such an event may lead to a new conflict situation due to dynamics of surrounding traffic aircraft [5,8]. Namely, some resulting maneuvers of the conflicting aircraft can induce new loss of SM with nearby aircraft in which new LAT for detection can be significantly reduced.…”

Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

“…In civil aviation, traffic alert and collision avoidance system (TCAS) is a universally accepted last-resort means of reducing the probability and frequency of mid-air collisions between aircrafts. [3][4][5] TCAS logic, too, has been researched. In recent years, there have been many studies on UAVs, such as ant colony algorithm, [6][7][8][9] dynamic programming, [10][11][12] genetic algorithm, [13][14][15][16] particle swarm optimization, [20][21][22] potential field algorithm, [23][24][25][26] colored petri net, 27 Markov decision process, 28 and combinations or optimizations of these algorithms.…”

Microstructural Characterization of Failed Aircraft Antenna

“…This has resulted in the development of advanced traffic collision and avoidance system (TCAS) by the Federal Aviation Administration (FAA), other countries' Civil Aviation Authorities (CAAs), and the aviation industry after many years of extensive research, developments and flight evaluation [1][2][3]. The TCAS assembly has been proposed by Morell [4].The TCAS is an assembly that provides a solution to the problem of reducing the risk of midair collision between aircrafts [1][2][3][4][5][6]. It is also known as Airborne Collision Avoidance System (ACAS) in international arena.…”