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Kalman filter implementation for small satellites using constraint GPS data

机译:使用约束GPS数据的小卫星的卡尔曼滤波器实现

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Due to the increased need for autonomy, an Extended Kalman Filter (EKF) has been designed to autonomously estimate the orbit using GPS data. A propagation step models the satellite dynamics as a two body with J2 (second zonal effect) perturbations being suitable for orbits in altitudes higher than 600 km. An onboard GPS receiver provides continuous measurement inputs. The continuity of measurements decreases the errors of the orbit determination algorithm. Power restrictions are imposed on small satellites in general and nanosatellites in particular. In cubesats, the GPS is forced to be shut down most of the mission's life time. GPS is turned on when experiments like atmospheric ones are carried out and meter level accuracy for positioning is required. This accuracy can't be obtained by other autonomous sensors like magnetometer and sun sensor as they provide kilometer level accuracy. Through simulation using Matlab and satellite tool kit (STK) the position accuracy is analyzed after imposing constrained conditions suitable for small satellites and a very tight one suitable for nanosatellite missions.
机译:由于增加了自主性需求,旨在使用GPS数据自主估计轨道的扩展卡尔曼滤波器(EKF)。传播步骤将卫星动力学模拟作为两个主体,其具有J2(第二个区间效果)扰动适用于高于600公里的高度轨道。板载GPS接收器提供连续的测量输入。测量的连续性降低了轨道确定算法的误差。特别是纳米卫星的小卫星对电力限制施加。在立方体中,GPS被迫关闭大部分任务的生命时间。当需要大气的实验和需要测量定位的仪表准确度时,GPS打开。这种精度不能通过磁力计和太阳传感器等自主传感器获得,因为它们提供公正的精度。通过使用MATLAB和卫星工具套件(STK)的仿真(STK)在施加适合于小卫星的受约束条件和适用于纳米卫星任务的非常紧密的位置后,分析位置精度。

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