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Air Traffic Management

Air Traffic Control (ATC) is responsible for managing the flow of aircraft operating within the National Airspace System (NAS). The Federal Aviation Agency (FAA) has projected that demands at the nation''s major airports may soon exceed capacity. Hence, the flow of traffic around airports is a major bottleneck to air traffic. Various researchers have investigated several concepts to enhance future Air Traffic Management. These include conflict detection and resolution, pilot intent inference, accurate aircraft trajectory predictions, and aircraft time-of-arrival predictions. A key requirement for implementation of the above concepts is an accurate knowledge of aircraft positions, velocities and flight modes. Furthermore, an accurate modeling of aircraft flight mode transitions is required for conformance monitoring under future ATM operations, such as Airborne spacing, trajectory based operations, and super density operations, under the Next Generation Air Transportation System (NextGen). Due to recent advances in navigation and data communication technologies such as the Global Positioning System (GPS), and a new data link between aircraft and between aircraft and ground controllers known as Automatic Dependent Surveillance-Broadcast (ADS-B), it may be plausible in the near future for aircraft to fly their own trajectories instead of predefined paths in the NAS. The research topics include:

  • Conflict detection and Resolution: Algorithms can detect and resolve multiple (>3) aircraft (or mobile robots conflicts with provable safety. Proposed algorithms could be implemented on-board autopilot for real-time applications as well as ground command stations as an decision supporting tools.
  • Pilot's intent inference and Conformance monitoring: An algorithm can infer the pilot's intent from kinematic data such as an aircraft's position and velocity, and information about flight plan, Air Traffic Control regulations, and the
    environment. Since the algorithm provides descriptive information as well as kinematic information of an aircraft, it
    could increase situational awareness of pilots or ground controllers and thus could lead to safe air traffic operations in congested traffic environments. The algorithm could be useful for aircraft conformance monitoring to see if aircraft follow their pre-planned schedules.
  • Airspace security monitoring and safety verification: Algorithms can detect potentially dangerous aircraft using the estimated behaviors of aircraft and the inferred pilot's intent, and thus alarm air traffic controllers or pilots to take necessary measures in time to prevent potential unsafe events. The algorithm could also be used for various military applications including airspace surveillance and multiple-aircraft tracking with capability of early warning.
  • Safe interface/cockpit design: Algorithms can check and/or design a cockpit interface such that it guarantees safety through reachable set analysis and computation. Especially, for unmanned aerial vehicles (UAVs) applications, current UAVs such as Predator are remotely piloted so that cockpit interface with increases situational awareness and guaranteed safety would be very important.
  • Dynamical Airspace Configuration: Design the future airspace systems that can accommodate both the manned and unmanned aircraft in the same airspace and also safely integrate future commercial aircraft-like spacecraft (e.g. spaceship one) into the air traffic control system.
  • Aircraft Separation Standards: Design the next generation separation standards that will possibly mitigate blunders, increase safety, and increase capacity.
  • ADS-B Integrity Monitoring: State Dependent Transition Hybrid Estimation (SDTHE) algorithm can be used to determine whether the automatic dependent surveillance-broadcast (ADS-B) system data is conforming to secondary surveillance radar(SSR) data.

 

We have developed an aircraft dynamic model for aircraft tracking in ATC based on the SLHS. The SDTHE algorithm is then used as the state estimation algorithm for tracking the aircraft positions/velocities and estimating the aircraft flight modes. A distinction of the SLHS model is that it can model aircraft which are following standard ATC flight routes or clearances, and is able to describe uncertianties in aircraft's flight mode transitions due to navigation uncertainties and unknown pilot intents. It has been shown that the proposed model/algorithm yields better tracking accuracy and mode estimation accuracy compared with other popular hybrid system models and hybrid estimation algorithms in ATC tracking. A comparison of the performance of the SDTHE algorithm and that of the IMM algorithm in a ATC tracking application is given in Figure 4 and Table 1. For the IMM algorithm, we have considered two designs (IMM1 and IMM2) with different constant mode transition probabilities. 

(a) Tracking error in horizontal plane. 

(b) Tracking error in vertical plane.

Figure 4: Comparison of tracking errors of the SDTHE algorithm and those of the IMM algorithm.



Table 1: Comparison of mode estimation errors of SDTHE algorithm versus those of the IMM algorithm

 

Fault Detection and Isolation

Apart from ATC operations and Air Traffic Management systems, many practical systems, such as embedded systems, multi-agent systems, and cooperative systems, are also best described by hybrid systems. The continuous dynamics of the hybrid systems could model the physical system dynamics and the discrete dynamics could represent the logical decision components. To enhance the reliability or safety of these systems, fault detection methods have been used to determine the occurrences of failures so that appropriate remedy actions can be taken.

One common approach for fault detection, known as the model-based approach, is illustrated in Figure 6. The fault detection problem can be divided into two steps: The first step is to design a filter based on a model of the plant to generate a vector known as the residual. The residual should ideally be zero (or zero mean) under no-fault conditions. The second step is to make decisions on whether a fault has occurred. This step is usually done using statistical tools to test if the residual has significantly deviated from zero. This fault detection method could be extended to fault detection and isolation (FDI) for multiple faults using a bank of residual generation filters in parallel.

It has been shown the the SLHS is useful in modeling practical hybrid systems that have stochastic discrete state (or mode) transitions which are depenedent on the continuous state. However, most existing quantatitive model-based fault detection methods, such as the observer-based methods or the Kalman filter methods, have so far considered systems with continuous state dynamics only. Hence, we have proposed a FDI algorithm for the SLHS. We have proposed an efficient residual generation filter for the SLHS. Furthermore, we have shown that the residual vector has a zero mean and a known covariance when the model matches the true system dynamics. Using the known statistical properties of the residual, we then designed a quantitative FDI scheme using hypothesis tests. We are currently investigating applications of the proposed FDI scheme in an aircraft autolanding system and a cooperative robotic system respectively.

 

 

Figure 7: A model-based fault detection scheme.