基于模糊自适应卡尔曼滤波的大气数据辅助姿态算法

 针对中低精度航姿参考系统(AHRS)在机体机动时不能利用加速度计修正水平姿态,以及噪声统计特性随实际工作情况变化的问题,提出了一种基于模糊自适应卡尔曼滤波的大气数据辅助姿态解算的方法。首先,考虑大气数据系统和航姿参考系统的优势,利用真空速、攻角和侧滑角等大气数据信息对非重力加速度进行补偿,以辅助水平姿态解算;其次,基于模糊自适应卡尔曼滤波原理,对观测模型的参数进行估计和修正,以实现水平姿态的最优估计;最后,选取某型飞机的试飞数据进行仿真验证。仿真结果表明,该方法可使飞机的水平姿态估计精度达到1.3°,且在偏差较大时有明显的纠偏作用。因此,相对于无机动加速度补偿和常规卡尔曼滤波来说,该方法能够更好地进行姿态估计,具有一定的实用价值。

Air data assisted attitude algorithm based on fuzzy adaptive Kalman filter

Aimed at solving problems that accelerometers cannot be utilized in maneuvering carriers to modify its horizontal attitude and that noise statistical properties change with the actual working conditions in low accuracy attitude and heading reference system (AHRS), an air data assisted attitude calculating method based on fuzzy adaptive Kalman filter is proposed. Firstly, for assisting horizontal attitude calculation, an attitude algorithm is presented to make use of air data, such as true airspeed, angle of attack and sideslip angle information to compensate maneuvering acceleration, combining the advantages of both air data system and AHRS. Secondly, estimating and modifying parameters of the observer model and system characteristics is processed based on fuzzy adaptive Kalman filter in order to realize optimal estimation of horizontal attitude. Finally, simulation of flight test data from a type aircraft flight is conducted. Simulation results demonstrate that the accuracy of attitude angels reaches 1.3 °, and it plays a significant role in correcting large deviations. Thus, to non-compensated maneuvering acceleration algorithm and conventional Kalman filter, this method is superior in attitude estimation and has practical value.