A Methodology for Collision Prediction and Alert Generation in Airport Environment 2016-01-1976
Aviation safety is one of the key focus areas of the aerospace industry as it involves safety of passengers, crew, assets etc. Due to advancements in technology, aviation safety has reached to safest levels compared to last few decades. In spite of declining trends in in-air accident rate, ground accidents are increasing due to ever increasing air traffic and human factors in the airport. Majority of the accidents occur during initial and final phases of the flight. Rapid increase in air traffic would pose challenge in ensuring safety and best utilization of Airports, Airspace and assets.
In current scenario multiple systems like Runway Debris Monitoring System, Runway Incursion Detection System, Obstacle avoidance system and Traffic Collision Avoidance System are used for collision prediction and alerting in airport environment. However these approaches are standalone in nature and have limitations in coverage, performance and are dependent on onboard equipment. There is a need to have an integrated solution for collision prediction and alerting to enhance the capacity and operational efficiency of the airports and airspace at the same time ensuring the safety of aircrafts and personnel.
This paper proposes a comprehensive, fool proof, integrated solution employing multiple sensor technologies to seamlessly predict collisions in all the zones of airport environment and generate alerts and guidance to prevent the same. Proposed system employs multiple sensor technologies like Optical and Infrared cameras, Laser Range finders, and Primary and Secondary surveillance radars for object monitoring, adopt latest technologies such as advanced image processing, sensor fusion for object detection and path tracking , collision prediction and synthetic visual environment for enhanced situational awareness and video based alert generation in real time. Simulation results of a synthesized data analysis that obtained through application of data fusion on multiple data sources and 3D path tracking algorithms are explained in the case study section.