十二表冗余捷联惯导系统数据融合技术研究

Study on Data Fusion for the 12-Sensor Redundant Strapdown Inertial Navigation System

针对采用正十二面体冗余仪表构型的十二表冗余捷联惯性导航系统,通过仿真和样机试验,开展了基于最小二乘估计的数据融合算法研究。对不同故障模式下的系统精度进行了分析,并通过Monte Carlo仿真验证了数据融合算法对提高系统导航精度的有效性。设计开展了样机静态导航试验,试验结果表明,理论最优的马尔柯夫估计并不完全适用于脉冲输出形式仪表的数据融合。最后通过优化改进加权系数、构造加权矩阵,显著提升了样机的静态导航精度,使样机位置误差和姿态误差与三表直接解算相比分别降低了78.6%和77.9%。

For the 12-sensor Redundant Strapdown Inertial Navigation System with a redundant configuration of dodecahedron, a study on the data fusion algorithm based on least square estimation is conducted, by simulation and prototype experiment. The system accuracy under different fault modes is analyzed, and the effectiveness of data fusion algorithm to improve the system navigation accuracy is verified by Monte Carlo simulation. The static navigation experiment of the prototype is designed and carried out. The experiment results show that the Markov estimator which is optimal in theory is not suitable for data fusion of impulse output instruments entirely. Finally, by improving the weight coefficients and constructing the weight matrix, the static navigation accuracy of the prototype is significantly improved: compared to the direct solution of three instruments, the position error and attitude error of the prototype decrease by 78.6% and 77.9% respectively.