平流层飞艇抗风场干扰CUKF算法设计与应用

为了提高风场干扰环境下飞艇的导航精度,研究飞艇抗风场干扰导航算法。在建立风场干扰条件下飞艇速度误差约束模型的基础上,设计抗风场干扰的约束Unscented卡尔曼滤波算法。首先确定风场干扰条件下飞艇的速度误差约束量,将该约束与Unscented卡尔曼滤波算法相结合,对速度误差进行估计和补偿,以减小风场对飞艇定位精度的影响；然后证明该算法的状态估计量不仅是无偏的,而且协方差小于标准UKF的协方差；最后将提出的算法应用于捷联惯导/天文/合成孔径雷达组合导航系统中进行仿真验证,并与自适应扩展卡尔曼滤波和抗差自适应UKF算法进行比较,结果表明：提出的约束UKF算法的滤波性能明显优于自适应EKF和抗差自适应UKF算法,能够有效抑制风场对飞艇定位精度的影响,提高飞艇的导航定位精度。

Design and Application on CUKF for Stratospheric Airship with Wind Field Disturbance

To improve the navigation accuracy of airship in the disturbance environment of wind field, a navigation algorithm which resist wind field disturbance was studied. Based on building up an error constraints model of the airship velocity in wind field disturbance, a constraints unscented Kalman filter algorithm which resist wind field disturbance was designed. Firstly, the error constraint vector of airship velocity was determined. To decrease the impact of wind field on positional accuracy of airship, the velocity error was estimated and compensated by the combination of unscented Kalman filter and error constraint vector. Then, the state estimation value of proposed algorithm was proved to be unbiased, and the covariance was less than that of the standard UKF. Finally, the proposed algorithm was applied to SINS/CNS/SAR integrated navigation system and conducted simulation, and was compared with the adaptive extended Kalman filter and robust adaptive UKF. The simulation results show that not only the filter performance of the proposed CUKF was much better than those of AEKF and RAUKF, but also the impact of wind filed on positional accuracy of was decreased and the navigation accuracy of airship was improved effectively.