Sensor classification and obstacle detection for aircraft external hazard monitoring
2008
This paper introduces a sensor-modeling framework to support the test and evaluation of External Hazard Monitor
configurations and algorithms within the Intelligent Integrated Flight Deck (IIFD). The paper, furthermore, examines
various runway hazards that may be encountered during aircraft approach procedures and classifies sensors that are
suited to detect these hazards. The work performed for this research is used to evaluate sensing technologies to be
implemented in the IIFD, a key component of NASA's Next Generation Air Transportation System. To detect objects on
or near airport runways, an aircraft will be equipped with a monitoring system that interfaces to one or more airborne
remote sensors and is capable of detection, classification, and tracking of objects in varying weather conditions. Physical
properties of an object such as size, shape, thermal signature, reflectivity, and motion are considered when evaluating the
sensor most suitable for detecting a particular object. The results will be used to assess the threat level associated with
the objects in terms of severity and risk using statistical methods based on the sensor's measurement and detection
capabilities. The sensors being evaluated include, airborne laser range scanners, forward looking infrared (FLIR), three
dimensional imagers, and visible light cameras.
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