Image-based positioning has not yet been certified in aviation applications. To cover numerous environmental conditions, the authors installed various optical sensors. They present an approach for fusing image data of two complementary cameras with different spectral ranges.
The use of two image sensors working in the visible light spectrum and infrared spectrum increases availability and accuracy, meeting requirements to be used as an augmentation for state-of-the art GNSS-based landing systems.
This investigation presents real flight data processed by means of the proposed method. This work constitutes a new approach for robust runway detection, since position calculation was only carried out once in one time epoch on a single blended image.
The proposed method was applied to data from two flight campaigns in post-process. A determined set of parameters lead to a sufficient level of availability and a valid runway detection throughout the final approach.
Citation
M. Angermann, S. Wolkow, A. Dekiert, U. Bestmann, P. Hecker (2018), “Linear blend: Data fusion in the image domain for image-based aircraft positioning during landing.” Pacific PNT Conference, www.ion.org/publications/browse.cfm
Aircraft navigation during landing approach is mostly supported by ground-based landing systems in commercial aviation, which cause high installation and maintenance costs.
Nevertheless, the final sequence of the flight before touchdown is mostly performed by the pilot manually, because of the high requirements for accuracy and integrity. Only a few landing systems can fulfill these requirements during the last 200 feet above ground.
The current work presents a further development of an optical positioning system to be deployed below 200 feet and on ground after touchdown in order to be used as an additional source for positioning information. The system is capable of visual 3D positioning of the aircraft relative to the runway.
Algorithms for threshold marking (see image below) and centerline detection, as well as lateral position calculation during rollout are presented. The system is evaluated during flight trials performed with the research aircraft Dornier Do 128-6.
Threshold marking detection: Extracted contours (blue), convex hulls (cyan), rotated rectangles (white), centroids of rectangles (red), horizontal line that crosses the most remaining candidates (magenta), base points of the threshold marking bars (orange), identified contours of the threshold marking and baseline (green). (Image: Authors)
Citation
S. Wolkow, M. Angermann, A. Dekiert, U. Bestmann (2018), “Model-based threshold and centerline detection for aircraft positioning during landing approach.” Pacific PNT Conference, www.ion.org/publications/browse.cfm