An improved method of SINS/CNS integrated navigation algorithm based on dual quaternions with gyro error corrected online

Min Yanling, Xiong Zhi, Xing Li, Liu Jianye, Yin Dequan
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引用次数: 1

Abstract

In consideration of the high dimension problem in the existing SINS/CNS integrated navigation system based on dual quaternions, the observable degree analysis method of system states based on singular value decomposition is introduced. According to the analysis result, the state components with good observability are estimated and the bad ones are removed, thus the computation time of Kalman filter is significantly decreased and the real-time performance of SINS/CNS is improved. To ensure that inertial navigation can also maintain high accuracy over a period of time when celestial auxiliary information is lost, an improved method of SINS/CNS integrated navigation algorithm based on dual quaternions with gyro error corrected online is proposed. The Kalman filter estimates constant gyro drift with the aid of celestial information and utilizes the calibration value to compensate gyro output online. The simulation results demonstrate that the proposed method can realize the effective estimation of constant gyro drift. In addition, the accuracy of inertial navigation is improved effectively.
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一种改进的基于对偶四元数的SINS/CNS组合导航算法,并在线校正陀螺误差
针对现有基于对偶四元数的SINS/CNS组合导航系统存在的高维问题,提出了基于奇异值分解的系统状态可观测度分析方法。根据分析结果,估计出可观测性较好的状态分量,去除可观测性较差的状态分量,从而大大缩短了卡尔曼滤波的计算时间,提高了SINS/CNS的实时性。为了保证惯性导航在天体辅助信息丢失一段时间内仍能保持较高的精度,提出了一种改进的基于对偶四元数的SINS/CNS组合导航算法,并对陀螺误差进行在线校正。卡尔曼滤波利用天体信息估计陀螺漂移,利用标定值在线补偿陀螺输出。仿真结果表明,该方法能够有效地实现陀螺恒漂移的估计。同时,有效地提高了惯性导航的精度。
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