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15 April 2008 Sensor classification and obstacle detection for aircraft external hazard monitoring
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Abstract
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark Smearcheck, Ananth K. Vadlamani, and Maarten Uijt de Haag "Sensor classification and obstacle detection for aircraft external hazard monitoring", Proc. SPIE 6957, Enhanced and Synthetic Vision 2008, 69570F (15 April 2008); https://doi.org/10.1117/12.784524
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