基于TLKF的单轴旋转SINS在线自标定方法

On-Line Self-Calibration Method for Single-Axis Rotary SINS Based on Two-Level Kalman Filter

针对单轴旋转捷联惯性导航系统(SINS)中轴向陀螺常值漂移无法被调制抵消的问题,提出一种轴向陀螺常值漂移在线自标定方法。对轴向陀螺常值漂移误差传播路径进行了分析,指出影响轴向陀螺常值漂移估计精度主要因素包括等效东向陀螺漂移、“数学平台”失准角、等效北向加速度计常值偏置等。建立了在线自标定Kalman滤波估计状态方程和量测方程,并设计了一种基于两级Kalman滤波的在线自标定流程。进行了计算机仿真和实际系统验证实验,实验结果表明,第二级Kalman滤波器能够较好地估计得到单轴旋转SINS轴向陀螺常值漂移及其标度因数误差,经过误差补偿后,其24h位置误差由6.71n mile减小为1.96n mile,提高了导航系统定位精度,满足中等精度SINS长时间导航需求。

Aiming at the problem that the constant drift of the axial gyroscope in the single-axis rotary strapdown inertial navigation system(SINS)cannot be compensated by modulation,an online self-calibration method of the constant drift of the axial gyroscope is proposed.The navigation error propagation path of constant drift of the axial gyroscope was analyzed,which pointed that the main factors affecting the estimation accuracy of constant drift of the axial gyroscope were equivalent eastern gyroscope constant drift,misalignment angle of the mathematical platform and the constant bias of equivalent northward accelerometer.The online self-calibration Kalman filter equations of state and measurement were established,and an online self-calibration flow based on two-level Kalman filter was designed.Computer simulation and actual system verification experiments were carried out.The experimental results show that the second-stage Kalman can estimate the constant drift and scale factor error of the single-axis rotary SINS.After error compensation,the 24 hours position error is reduced from 6.71 n miles to 1.96 n miles,which improves the positioning accuracy of navigation system and meets the long-term navigation needs of medium precision SINS.