Simulation and Performance Evaluation of Algorithms for Unmanned Aircraft Conflict Detection and Resolution

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency

Simulation and Performance Evaluation of Algorithms for Unmanned Aircraft Conflict Detection and Resolution

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency

Multi-aircraft conflict detection and resolution based on probabilistic reach sets

In this brief, a novel scheme to multi-aircraft conflict detection and resolution is introduced. A key feature of the proposed scheme is that uncertainty affecting the aircraft future positions along some look-ahead prediction horizon is accounted for via a probabilistic reachability analysis approach. In particular, ellipsoidal probabilistic reach sets are determined by formulating a chance-constrained optimization problem and solving it via a simulation-based method called scenario approach. Conflict detection is then performed by verifying if the ellipsoidal reach sets of different aircraft intersect. If a conflict is detected, then the aircraft flight plans are redesigned by solving a second-order cone program resting on the approximation of the ellipsoidal reach sets with spheres with constant radius along the look-ahead horizon. A bisection procedure allows one to determine the minimum radius such that the ellipsoidal reach sets of different aircraft along the corresponding new flight plans do not intersect. Some numerical examples are presented to show the efficacy of the proposed scheme

Investigation of Alert Zone and Display Concepts for Free Flight

To better understand the potential benefits and drawbacks of utilizing a probability-based method, a number of alternative approaches to the collision avoidance problem were examined and summarized in the paper "Survey of Conflict Detection and Resolution Modeling Methods" which was presented in August 1997 at the AIAA Guidance, Navigation, and Control Conference. The paper provides a summary and comparative evaluation of the many different approaches that have been used in the past to perform conflict analysis. Each method is categorized in its dynamic modeling approach and method of handling conflict detection and conflict resolution. For example, one category included the extrapolation method used to predict future trajectories of which 3 were defined: nominal, probabilistic, and worst-case. Another category listed the metrics and parameters used by each method to make conflict decisions (i.e. estimated time to closest point of approach, miss distance, current separation, expected maneuvering cost, probability of conflict). Other useful information such as the ability to handle multi-aircraft conflicts and cooperative and non-cooperative maneuvering is also included

Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance

Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems

An Analysis of the Spatio-Temporal Factors Affecting Aircraft Conflicts Based on Simulation Modelling

The demand for air travel worldwide continues to grow at a rapid rate, especially in Europe and the United States. In Europe, the demand exceeded predictions with a real annual growth of 7.1% in the period 1985-1990, against a prediction of 2.4%. By the year 2010, the demand is expected to double from the 1990 level. Within the UK international scheduled passenger traffic is predicted to increase, on average, by 5.8 per cent per year between 1999 and 2003. The demand has not been matched by availability of capacity. In Western Europe many of the largest airports suffer from runway capacity constraints. Europe also suffers from an en-route airspace capacity constraint, which is determined by the workload of the air traffic controllers, i.e. the physical and mental work that controllers must undertake to safely conduct air traffic under their jurisdiction through en-route airspace. The annual cost to Europe due to air traffic inefficiency and congestion in en-route airspace is estimated to be 5 billion US Dollars, primarily due to delays caused by non-optimal route structures and reduced productivity of controllers due to equipment inefficiencies. Therefore, to in order to decrease the total delay, an increase in en-route capacity is of paramount importance. At a global scale and in the early 1980s, the International Civil Aviation Organisation (ICAO) recognised that the traditional air traffic control (ATC) systems would not cope with the growth in demand for capacity. Consequently new technologies and procedures have been proposed to enable ATC to cope with this demand, e.g. satellite-based system concept to meet the future civil aviation requirements for communication, navigation and surveillance/ air traffic management (CNS/ATM). In Europe, the organisation EUROCONTROL (established in 1960 to co-ordinate European ATM) proposed a variety of measures to increase the capacity of en-route airspace. A key change envisaged is the increasing delegation of responsibilities for control to flight crew, by the use of airborne separation assurance between aircraft, leading eventually to ?free flight? airspace. However, there are major concerns regarding the safety of operations in ?free flight? airspace. The safety of such airspace can be investigated by analysing the factors that affect conflict occurrence, i.e. a loss of the prescribed separation between two aircraft in airspace. This paper analyses the factors affecting conflict occurrence in current airspace and future free flight airspace by using a simulation model of air traffic controller workload, the RAMS model. The paper begins with a literature review of the factors that affect conflict occurrence. This is followed by a description of the RAMS model and of its use in this analysis. The airspace simulated is the Mediterranean Free Flight region, and the major attributes of this region and of the traffic demand patterns are outlined next. In particular a day?s air traffic is simulated in the two airspace scenarios, and rules for conflict detection and resolution are carefully defined. The following section outlines the framework for analysing the output from the simulations, using negative binomial (NB) and generalised negative binomial (GNB) regression, and discusses the estimation methods required. The next section presents the results of the regression analysis, taking into account the spatio-temporal nature of the data. The following section presents an analysis of the spatial and temporal pattern of conflicts in the two airspace scenarios across a day, highlighting possible metrics to indicate this. The paper concludes with future research directions based upon this analysis.

A New Approach To Estimate The Collision Probability For Automotive Applications

We revisit the computation of probability of collision in the context of automotive collision avoidance (the estimation of a potential collision is also referred to as conflict detection in other contexts). After reviewing existing approaches to the definition and computation of a collision probability we argue that the question "What is the probability of collision within the next three seconds?" can be answered on the basis of a collision probability rate. Using results on level crossings for vector stochastic processes we derive a general expression for the upper bound of the distribution of the collision probability rate. This expression is valid for arbitrary prediction models including process noise. We demonstrate in several examples that distributions obtained by large-scale Monte-Carlo simulations obey this bound and in many cases approximately saturate the bound. We derive an approximation for the distribution of the collision probability rate that can be computed on an embedded platform. In order to efficiently sample this probability rate distribution for determination of its characteristic shape an adaptive method to obtain the sampling points is proposed. An upper bound of the probability of collision is then obtained by one-dimensional numerical integration over the time period of interest. A straightforward application of this method applies to the collision of an extended object with a second point-like object. Using an abstraction of the second object by salient points of its boundary we propose an application of this method to two extended objects with arbitrary orientation. Finally, the distribution of the collision probability rate is identified as the distribution of the time-to-collision.Comment: Revised and restructured version, discussion of extended vehicles expanded, section on TTC expanded, references added, other minor changes, 17 pages, 18 figure