Development of a Navigation Solution for an Image Aided Automatic Landing System

被引:0
|
作者
Eitner, Christian A. [1 ]
Holzapfel, Florian [1 ]
机构
[1] Tech Univ Munich, Inst Flight Syst Dynam, D-80290 Munich, Germany
关键词
D O I
暂无
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The probability of a longitudinal or lateral runway overrun can be translated into requirements for the lateral and vertical positioning guidance during the final approach. To be able to safely land within the prescribed touchdown zone the accuracy of the lateral and vertical landing guidance of conventional board autonomous navigation systems using e. g. augmented satellite navigation systems (WAAS, EGNOS) is not sufficient and must be augmented by additional sensor data to comply with strict accuracy and integrity requirements. In this article a new concept for improving the navigation positioning accuracy and reliability during the final stages of an automatic UAV or aircraft landing scenario is presented. The basic scenario consists in a UAV automatic landing system using board autonomous sensor data and little or no specific landing guidance ground infrastructure. Optical sensor data of an on-board monocamera system is used to detect the inclination angles of the runway borderlines. If the runway width can be considered a known parameter, the detection of the lateral borderlines is sufficient to calculate the lateral and vertical displacement of the UAV relative to the runway coordinate system, thereby augmenting the position solution for the lateral and vertical displacement relative to the approach trajectory. The implemented optical measurement equation describes a direct relationship between geometric image features and the position solution and is based only on the detection of parallel runway lines. The equation uses knowledge about the attitude parameters of the UAV and makes therefore optimal usage of all known and relevant system parameters. The optical measurement equation is implemented in the framework of an error-state-space Kalman filter. Results using a closed loop simulation environment that incorporates the UAV system dynamics, flight control and data fusion algorithms and a synthetic camera image generation are presented in the remainder of the article.
引用
收藏
页码:879 / 891
页数:13
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