一种融合 OBA/卡尔曼滤波的惯导行进间抗扰粗对准方法

Aiming to address the susceptibility of traditional inertial/odometer integrated in-motion coarse alignment algorithms to disturbances from odometer errors and special operating conditions such as acceleration and deceleration, a disturbance rejection in-motion coarse alignment method for odometer-aided strapdown inertial navigation system (SINS) by integrating optimization-based alignment (OBA)/Kalman filtering is proposed. By adjusting the differential interval of odometer speed estimation, an asynchronous frame incremental sampling method is designed to enhance the accuracy of speed estimation. Utilizing the observation vector constructed by the OBA coarse alignment method, an error estimation model for misalignment angles and odometer scale factors is established, facilitating rapid in-motion alignment and scale factor calibration of vehicle-borne equipment. In vehicular experiments, the in-motion coarse alignment time is reduced by an average of 16 s compared to the traditional OBA method, while the heading accuracy improves by 40.2% within a 3-minute alignment period. The proposed improved coarse alignment algorithm offers faster convergence rates and higher alignment accuracy, effectively identifying and compensating for odometer scale factor errors during the coarse alignment stage.