【摘要】 平流层飞艇可携带大载荷实现长时间悬停,是理想的高分辨率对地观测平台。飞艇悬停需要对环境风场和飞行参数进行精确地感知,因此对环境风场的感知与飞艇自身状态的估计是定点悬停的一大难点。传统测量手段测量精度低,不能解决风场与飞艇自身运动造成的数据耦合问题。针对上述问题,研究了一种综合惯导、GPS和风速风向仪数据,根据飞行动力学方程,利用无迹卡尔曼滤波来估计风场环境和自身状态的方法。上述方法克服了普通直接测量元件精度较低的问题。根据验证飞行获取的数据进行的仿真表明,上述方法估计精度高,实时性好,具备较高的工程应用价值。更多还原

【Abstract】 A stratospheric airship can hover in a certain position for a long time with heavy loads,which is an ideal platform for high resolution earth-observation. Hovering requires accurate perception of wind field and flight parameters,so the perception of the wind field and the estimation of the airship’s own state are major difficulty in fixing the hover. The traditional measurement method has low measurement accuracy and cannot solve the problem of data coupling between the wind field and the airship’s own movement. Aiming at the above problems,a method depending on data from inertial navigation system,GPS and anemometer was introduced based on nonlinear dynamic equation and UKF. The problem of low accuracy of ordinary direct measurement components was overcome. The flight test shows that the method has high accuracy and real-time performance,which is of great value to engineering.更多还原