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.

The process of developing and certifying aircraft and aeronautical systems requires the execution of experimental flight test campaigns to determine the actual characteristics of the system being developed and/or validated. In this process, there are many campaigns that are inherently dangerous, such as the store separation. In this particular case, the greatest risk is the collision of the store with the fuselage of the aircraft. To mitigate the risks of this campaign, it is necessary to compare the actual trajectory of a separation with its simulated estimates. With such information, it is possible to decide whether the next store release can be done with the required safety and/or whether the model used to estimate the separation trajectory is valid or not. Consequently, exact determination of the trajectory of the separation is necessary. Store separation is a strategic, relevant, and complex process for all nations. The two main techniques for determining the quantitative store trajectory data with 6DoF (six degrees of freedom) are photogrammetry and instrumented telemetry packages (data obtained from inertial sensors that are installed in the store). Each presents advantages and disadvantages. In regard to photogrammetry, several market solutions can be used to perform these tests. However, the result of the separation trajectory is only obtained after the test flight, and therefore, it is not possible to safely carry out more than one on the same flight. In this context, the development and validation of a solution that will allow the realization of near real-time separation analysis are in fact an innovative and original work. This paper discusses the development and validation, through actual static ejection tests, of the components that will compose a new onboard optical trajectory system for use in store separation campaigns. This solution includes the implementation of a three-dimensional (3D) calibration field that allows calibration of the optical assembly with just one photo per optical assembly, development of a complete analytical model for camera calibration, and development of specific software for identification and tracking of targets in two-dimensional (2D) coordinate images and three-dimensional (3D) coordinate trajectory calculation. In relation to the calibration, the analytical model is based on a pinhole type camera and considers its intrinsic parameters. This allowed for a mean square error smaller than ±3.9 pixels @1σ. The 3D analysis software for 6DoF trajectory expression was developed using photogrammetry techniques and absolute orientation. The uncertainty associated with the position measurement of each of the markers varies from ±0.02 mm to ±8.00 mm @1σ, depending on the geometry of the viewing angles. The experiments were carried out at IPEV (Flight Test Research Institute)/Brazil, and the results were considered satisfactory. We advocate that the knowledge gained through this research contributes to the development of new methods that permit almost real-time analysis in store separation tests.

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