| Abstract | The National Research Council of Canada has conducted feasibility studies into the development of non-intrusive flight test instrumentation methods with the goal of reducing the cost and time-to-market for certified aerospace products. Video recognition for the collection of flight test time history data was one such non-intrusive method. The advantages of using machine vision for flight data collection are many. One video camera can be used to extract data for many in-flight parameters, reducing instrumentation time, the airworthiness effort, the overall aircraft schedule and associated costs. This paper details the development of flight test video recognition software, calibration algorithms, hardware, and the accuracy of data collected by video via full flight simulator data benchmarks. Video recognition is a convenient means of collecting cockpit flight test data for model development and certification of full flight simulator devices. |
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