Air Traffic Control

In Air Traffic Control (ATC), our research focuses on aviation safety and security. We also analyze airspace to assist trajectory planning, anomaly detection in the airspace, aviation scheduling etc. In addition to this, develop automation algorithms and supporting tools.

Projects undertaken in ATC include:

• Safe Separation Assurance

• Intent-Based Probabilistic Conflict Detection

• Probabilistic Trajectory Prediction and Conflict Detection

• Protocol-based Conflict Resolution

• Probabilistic Conflict Resolution

• Data-Driven Conflict Resolution Generator

• Aircraft Tracking and 4D Trajectory Prediction

• Intent-Based Trajectory Prediction

• Estimated Time of Arrival Prediction in Terminal Airspace

• Airspace/Airport Conformance Monitoring

• Sequencing and Scheduling

• Throughput Factor Analysis

• Dynamic Airspace Configuration

• Human-Machine Interaction (HMI) Issues

• Intent-Based Mode-Confusion Detection

• Intent-Based Mode-Confusion Detection (Formal Verification)

• Data-Driven Modeling and Analysis Framework for HMI

• Intent-Based Mode-Confusion Detection (Learning-based)

• Anomaly Detection in National Airspace System (NAS)

• safe separation assurance,

• aircraft tracking and 4D trajectory prediction,

• airspace/airport conformance monitoring and safety verification,

• Modeling the National Airspace System (NAS) with Uncertainty, and

• Dynamic Airspace Reconfiguration.

• Formal Verification and Validation (V&V) for Flight Deck,

• Human-Machine Interaction (HMI) Issues (e.g., pilot’s wrong interaction with autopilot in the cockpit) detection, and

• Big Data analytics for Large-Scale Flight Data Analysis using data mining and machine learning techniques.