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This paper proposes a method for multiple-aircraft conflict avoidance. We assume that aircraft cruise at constant altitude with varying velocities and that conflicts are resolved in the horizontal plane using heading change, velocity change, or a combination thereof. We assume that each aircraft's position, heading, and velocity are available to all aircraft involved in the conflict, we constrain the maneuver to be two straight paths of equal length, and we assume that all aircraft initiate conflict resolution maneuvers at the same time and that once an aircraft has initiated a maneuver, its velocity along the maneuver remains constant. Our multiple-aircraft conflict resolution methodology is presented in two steps; first, we consider an unrealistic but geometrically simple exact conflict, in which the original trajectories of all aircraft collide at a point, in order to derive a closed-form analytic solution for the required heading change, and then we consider a realistic inexact conflict, in which conflict points of multiple aircraft do not coincide. Heading change is a main control input for conflict resolution, yet velocity change is also used for an inexact conflict. We then construct a finite partition of the airspace around the conflict, and using our analytic solution, we derive a protocol for resolving the worst-case conflict within each partition. The result is a multiple-aircraft conflict resolution protocol, or a simple rule which is easily understandable and

As the technological capabilities of automated systems have increased, the use of unmanned aerial vehicles (UAVs) for traditionally exhausting and dangerous manned missions has become more feasible. The United States Army, Air Force, and Navy have released plans for the increased use of UAVs, but have only recently shown interest in the cyber security aspect of UAVs. As a result, current autopilot systems were not built with cyber security considerations taken into account, and are thus vulnerable to cyber attack. Since UAVs rely heavily on their on-board autopilots to function, it is important to develop an autopilot system that is robust to possible cyber attacks. In order to develop a cyber-secure autopilot architecture, we have run a study on potential cyber threats and vulnerabilities of the current autopilot systems. This study involved a literature review on general cyber attack methods and on networked systems, which we used to identify the possible threats and vulnerabilities of the current autopilot system. We then studied the identified threats and vulnerabilities in order to analyze the post-attack behavior of the autopilot system through simulation. The uses of UAVs are increasing in many applications other than the traditional military use. We describe several example scenarios involving cyber attacks that demonstrate the vulnerabilities of current autopilot systems.

To efficiently and safely accommodate the ever increasing air traffic, the concept of the Next Generation Air Transportation System has been proposed and studied in recent years. In this paper, we consider the problem of four-dimensional trajectory prediction and conflict detection, which is one of the key functions of the Next Generation Air Transportation System. A stochastic linear hybrid system is proposed to describe the dynamics of an aircraft with changing flight modes. The stochastic linear hybrid system can have two different discrete-state transition models depending on the availability of flight plans (or aircraft intent): the Markov transition model and state-dependent transition model. The state-dependent transition model can incorporate the prior information about an aircraft's intent. Based on the proposed model, an algorithm for the probabilistic reachability analysis of the stochastic linear hybrid system is proposed for aircraft four-dimensional trajectory prediction. To detect a midair conflict between aircraft, a computationally efficient algorithm is developed based on the cumulative distribution function approximation for the quadratic form of Gaussian random variables. The performance of the proposed algorithms is validated through an illustrative air traffic scenario. Nomenclature= discrete state of a hybrid system T = look-ahead time horizon in trajectory prediction T s = sampling time v = observation noise w = process noise x = continuous state of a hybrid system z = radar observations/predicted observations = Markov transition matrix = discrete-state transition function ; ; h = coordinate in the local navigation frame

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