Feature detection for Unmanned Aircraft Systems (UAS) sense and avoid scenarios is a crucial preliminary step for target detection. Its importance culminates when distant (pixel size) targets representing incoming aircraft are considered. This paper presents an experimental evaluation of four popular feature detection methods using flight test data and based on evaluation criteria such as first detection distance and percentage of frames with detected target features. Our results show that for close range targets all four methods have similar performance, while for distant (pixel-size) targets, the Shi and Tomasi method outperforms the other three methods (Harris-Stephens-Plessey, SUSAN and FAST).
This paper describes an investigation of a workload measurement technique based on pilot control movements. The Dynamic Interface Modeling and Simulation System Product Metric (DIMSS PM) assumes that pilot control activity can be used to evaluate pilot workload. Three qualified test pilots flew the fly-bywire NRC Bell 205 helicopter in a short test program that compared the DIMSS PM with subjective workload ratings and handling qualities ratings. The pilots performed a variation of an ADS-33E bob-up with varying levels of simulated turbulence and modified cyclic control characteristics. Good agreement was found for most in-flight test conditions between DIMSS Workload Metric scores and subjective workload ratings from the Bedford Workload Scale and Cooper-Harper handling qualities ratings. While, the DIMSS Workload Metric did not accurately reflect workload increases due to variations in the cyclic stick characteristics, the metric shows promise as an objective measurement tool of pilot workload in well-defined tests.
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