一种冗余惯性导航系统的非线性初始对准模型

通过合理的冗余配置惯性导航系统中惯性传感器,充分利用冗余惯性传感器的重复量测值,提出了一种非线性初始对准模型。该模型直接利用惯性传感器的量测输出作为观测值,3个姿态角作为状态变量,有效的降低了模型的维数和提高了模型的可观测度,从而保证了初始对准的精度和速度。通过分析表明,初始对准的稳态精度取决于惯性传感器的常值误差。仿真结果表明,非线性初始对准模型对准过程中的稳态精度与传统的初始对准模型的稳态精度相当,但是在实时性方面,它能够在5s内使3个失准角同时收敛。

A nonlinear initial alignment model for redundant inertial navigation system

Through reasonable redundant configuring the inertial sensors in the inertial navigation system and taking full advantage of the redundant inertial sensor output data, a nonlinear initial alignment model is proposed. The outputs of the inertial sensors and the three attitude angles are treated as observations and state variables respectively in the proposed nonlinear model. Due to the nonlinear model, the dimension of the model is reduced and the observable degree of the model is improved effectively, the initial alignment accuracy and speed is guaranteed. The analysis shows that the steady-state accuracy of the initial alignment depends on the constant error of the inertial sensors. Simulation results show that the steady-state accuracy of the nonlinear model is similar to the traditional model, but the nonlinear model can make three misalignment angles convergent in 5 second.