Aircraft Trajectory Prediction
By Cameron Sheridan
The purpose of this review is to identify and analyse work that is currently being done on aircraft trajectory prediction (ATP); particularly the approach of modern day researchers to the problematic issue of the growingly clustered airspace. The benefits of this review include the exploration of several sub-topics of the literature. Through examining the current methods towards trajectory modelling validation and the techniques that are now employed to neutralise error sources, it was found that with the modern-day approaches an algorithm and its trajectory prediction (TP) can be assessed and consequently improved upon. A number of systems pertinent to conflict are discussed and results are presented which illustrate and compare the effectiveness of heading and altitudinal resolution manoeuvres. Additionally, a number of recent developments and innovations in the field pertinent to the technologies and techniques used are discussed, thus illustrating a clear indication of research still moving forward in this field. II. Introduction
An ATP is a ‘mapping of points over a time interval [a,b] to the space R³’ (Tastambekova et al. 2010, p.2). Although this is correct in many senses, this explanation fails to acknowledge the intricacy and designed purpose. More accurately, a TP module has the capacity to calculate the future flight path of an aircraft given that it has been supplied with the required data, i.e. the flight intent, an aircraft performance model, and finally, an estimation of the future atmospheric/environmental conditions (Swierstra and Green 2004). An aircraft trajectory is a future path of an aircraft that can be represented visually in three forms: 2D, 3D and 4D (x, y, altitude and time) with 4D the more frequently used nowadays by air traffic control (ATC) and air traffic management (ATM) due to its far more realistic representation and ease of interpretation (Vivona et al. 2010; Poretta et al. 2010; Paglione and Oaks 2009). The significance of ATP is certainly appreciated. There is support for the importance of TP and the role it plays in advanced ATM operations, especially with a growingly clustered airspace in the next decade (Lee et al. 2010; Porretta et al. 2010 and Denery et al. 2011). The most crucial function of a TP however, as viewed by Lymperopoulos and Lygeros (2010), is to supply advice to ATC. Consequently, they can then make well-informed executive judgments to ensure the safety and effectiveness of our airspace. The purpose of this study is to inform what is happening in this field through examination of both the developments within ATP and the current problems facing researchers: namely, the significant increase in air-traffic by 2025. This will be done through exploring recent literature in this field that pertains to: conflict detection and resolution; the technologies and techniques involved; and, the error sources that are involved with a prediction and their subsequent effect on the uncertainty of a prediction. III. Modelling Validation and Uncertainties
Efficiency and accuracy are two central points of this literature, which alone could be considered as the determining factors of a respectable TP model; thus, sufficient research is required to improve both, without the sacrifice of one. How does one validate the performance of an algorithm and whether its TP is ‘accurate’? The common answer it seems (Anonymous 2010 and Paglione and Oaks 2007, pp. 2) is through the degree of conformity between the measured or predicted data and the true data of an aircraft at a given time. A. Uncertainties
Figure 1: Paglione and Oaks (2009)
Figure 1: Paglione and Oaks (2009)
Uncertainties are perhaps the biggest hurdle in further advancements in this field. Obviously, as the prediction increases in time, the uncertainties of the flight begin to take effect – up to a point where the trajectory...