Autonomous Sensor-based Landing Systems: Fusion of Vague and Incomplete Information by Application of Fuzzy Clustering Techniques

Abstract

Enhanced Vision Systems (EVS) are currently developed with the goal to alleviate restrictions in airspace and airport capacity in low-visibility conditions. EVS relies on weather penetrating forward-looking sensors that augment the naturally existing visual cues in the environment and provide a real-time image of prominent topographical objects that may be identified by the pilot. In this paper an automatic analysis of millimetre wave radar images for Enhanced Vision Systems is presented. The core part of the system is a fuzzy rule based inference machine which controls the data analysis based on the uncertainty in the actual knowledge in combination with a-priori knowledge. Compared with standard TV or IR images the quality of MMW images is rather poor and data is highly corrupted with noise and clutter. Therefore, one main task of the inference machine is to handle uncertainties as well as ambiguities and inconsistencies to draw the right conclusions. The output of different sensor data analysis processes are fused and evaluated within a fuzzy/possibilistic clustering algorithm whose results serve as input to the inference machine. The only a-priori knowledge used in the presented approach is the same pilots already know from airport charts which are available of almost every airport. The performance of the approach is demonstrated with real data acquired during extensive flight tests to several airports in Northern Germany.