为了将惯性凝固思想延伸到行进间精对准中并提升计算效率，提出了基于地球系的间接精对准算法。描述了地球系下的捷联惯导/里程计系统模型和相应的Ψ角误差模型。考虑安装偏差角、杆臂等因素，建立了相应的卡尔曼滤波方程。六组行车轨迹的行进间对准结果表明，相对于地理系精对准算法，地球系间接算法整体对准性能更加优越，系统的稳定性和快速性得到提高，在对准第600 s方位失准角可以稳定在1 mil(1σ)的误差限内。另外，地球系滤波算法具有更好的初始化参数适应性，有利于工程实现。%For the purpose of extending the application of inertia freezing methodology into moving-base fine-alignment and promoting computational efficiency, an indirect fine-alignment algorithm is presented which introduces earth-centered and earth-fixed (ECEF) frame as intermediate reference. The mathematical formulation of the inertial sensor/odometer integration system is given within ECEF frame, and the correspondingψ-angle error model is outlined. By taking the SINS-vehicle misaligning angle and lever arm into account, the derivation of the appropriate Kalman filter (KF) is discussed. The proposed algorithm is verified through experimental data collected from six traveling routes. In comparison with geographic-frame KF algorithm, the ECEF-frame indirect approach has shorter stabilizing time and performs better in robustness, and a better overall accuracy of 1 mil(1σ) azimuth error within 600 s can be achieved. The ECEF-frame KF is less sensitive to changes of initialization parameters, which is useful for practical application.
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