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

机译:用于图像辅助自动着陆系统的导航解决方案的开发

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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.
机译:在最终方法期间,可以将纵向或横向跑道超支的概率转换为对横向和垂直定位引导的要求。为了能够在规定的触地区安全地防止常规板自主导航系统的横向和垂直着陆引导的准确性。增强卫星导航系统(WAAS,EGNOS)是不够的,必须通过额外的传感器数据来增强,以符合严格的准确性和完整性要求。在本文中,提出了一种新的概念,用于提高在自动UAV或飞机着陆场景的最终阶段期间的导航定位精度和可靠性。基本情况包括使用董事会自动传感器数据的UAV自动着陆系统,很少或没有具体的着陆引导地面基础设施。板载Monocamera系统的光学传感器数据用于检测跑道边界线的倾斜角度。如果跑道宽度可以被认为是已知的参数,则横向边缘线的检测足以计算UAV相对于跑道坐标系的横向和垂直位移,从而增强相对于横向和垂直位移的位置解决方案接近轨迹。实现的光学测量方程描述了几何图像特征和位置解决方案之间的直接关系,并且仅基于并行跑道线的检测。该等式使用关于UAV的姿态参数的知识,并使得所有已知和相关系统参数的最佳使用。光学测量方程在错误状态空间卡尔曼滤波器的框架中实现。结果使用闭环仿真环境,其中包括UAV系统动态,飞行控制和数据融合算法以及合成摄像机图像的生成。

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