In this paper, the recognition information in aircraft images of Head-Up Display (HUD) was made using artificial neural network (ANN) and a correlation algorithm. During the flight tests, the images displayed on the HUD could be stored for later analysis. HUD images presents many aircraft data provided by its avionics system (e.g. altitude, feet, time). Therefore, HUD images are a primary source of information for most aircraft and pilots, especially in military missions. At IPEV (Flight Test & Research Institute), the extraction of information from HUD images is performed manually, frame by frame, for later analysis. The big issue is that in one hour of flight test about 36,000 frames are generated. Therefore, data extraction becomes complex, time consuming and prone to failures. To reduce these problems, the IPEV developed an algorithm that load HUD images and then partitions the images in regions that were classified, recognized and converted into text by using ANN and a correlation algorithm. The development of the algorithm is presented in this paper.
A new Global Positioning System (GPS) Attitude Determination Algorithm (GADA) is proposed, featuring the capability to keep its accuracy, even when the Line-of-Sight Angle (LOS) of a given Satellite Vehicle (SV) is below the GPS Horizontal Antenna Plane (HAP).The GADA model has been developed and evaluated through simulations and flight test campaigns, which comprised static and dynamic flight profiles, to best characterize the algorithm performance. As attitude reference a complete Flight Tests Instrumentation (FTI) system was integrated into the testbed for the flight test campaign. The attitude measurements given by GADA and REQUEST algorithms are compared with those given by FTI (ie., reference system). The results show that GADA accuracy is significantly better than that of REQUEST, for all flight conditions. IEEE A&E SYSTEMS MAGAZINE, DECEMBER 20073
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