Based on the motion system and visual system design of flight simulator, the software and hardware design in motion system and visual system of flight simulator is regared as key problem. The fidelity automatic test system is discussed. The aim is to explore a complex system integration method. The fidelity automatic test system is used to detect the fidelity of motion system and visual system. The results show that the design of motion and visual system is reasonable and the performance can meet demand of fidelity.
High-resolution remote sensing images are the key data source for the visual system of a flight simulator for training a qualified pilot. However, due to hardware limitations, it is an expensive task to collect spectral and spatial images at very high resolutions. In this work, we try to tackle this issue with another perspective based on image super-resolution (SR) technology. First, we present a new ultra-high-resolution remote sensing image dataset named Airport80, which is captured from the airspace near various airports. Second, a deep learning baseline is proposed by applying the generative and adversarial mechanism, which is able to reconstruct a high-resolution image during a single image super-resolution. Experimental results for our benchmark demonstrate the effectiveness of the proposed network and show it has reached satisfactory performances.
Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline