车载惯导系统抗扰动自对准方法对比研究

Comparative Study on Anti Disturbance Self Alignment Methods for Inertial Navigation System on Vehicles

关键词:  奇异值分解（SVD）  惯性系  抗扰动  对准

Key words:  SVD  Inertial frame  Anti disturbance  Alignment

摘要:

自对准技术是陆基机动平台及装备用惯导系统的一项关键技术，其核心在于惯导系统如何在风扰、发动机振动、人员走动等各种干扰下实现快速、高精度对准。针对目前常用的惯性系解析自对准、Kalman滤波估计自对准和惯性系奇异值分解（SVD）自对准等三种方法进行了研究和对比分析，根据三种对准方法各自的实现原理，提出了一种有针对性的抗扰动优化方法，并通过了数学仿真分析和车载试验验证。结果表明，在车载使用环境下，后两种方法都能获得较为准确的姿态信息，惯性系SVD方法可以达到与Kalman滤波方法相当的对准精度和收敛速度，且无需先验信息即可获得最优解，表现出了较好的工程应用优势。

Abstract:

Self alignment technology is a key technology of Inertial Navigation Systems(INS) for land-based mobile platforms and weapons. Its core lies in how the inertial navigation system can achieve rapid and high-precision alignment under various disturbances such as wind disturbance, engine vibration and personnel walking. In this paper, three commonly used methods of inertial frame analytical self-alignment, Kalman filter estimation self-alignment and inertial frame SVD self-alignment are studied and compared. According to the realization principles of the three alignment methods, a targeted anti-interference optimization method is proposed, which is verified by mathematical modeling simulation analysis and vehicle experiment. The results show that the latter two methods can obtain more accurate attitude information in vehicle environments. The inertial frame SVD method can achieve the same alignment accuracy and convergence speed as the Kalman filter method, and can obtain the optimal solution without prior information, which shows good engineering application advantages.