基于改进最小二乘支持向量机的惯性测量组件故障在线检测方法

Fault online test method of inertial measurement units based on improved LS-SVM

为提高惯导系统工作的可靠性和导航性能,对其惯性测量组件的故障模式和检测模型进行了分析。针对最小二乘支持向量机(LS-SVM)回归算法做了两点改进,具体方法是先对输入样本观察窗平移更新的每个样本数据进行异常点滤波判断并用牛顿插值法进行处理,接着通过对在线LS-SVM回归过程的研究,提出了一种递推求解的快速算法,将惯性测量组件的输出量、舵偏角改变量并辅以环境因素作为观测样本序列,应用该算法来提高模型检测的准确性和时效性。最后对惯性测量组件无故障和出现卡滞、恒偏差时的故障模式进行了仿真实验,结果表明,与应用LS-SVM、SVM和BP神经网络算法相比,提出的惯性测量组件故障在线检测方法具有较强的鲁棒性和较快的速度。

In order to improve the reliability and navigation performance of the inertial navigation system, the fault mode and test model were analyzed. Two ameliorations were made for the method of the online least squares support vector machine(LS-SVM): 1) The singularity was found out and disposed with Newton interpolation method among the sample data which was shifted and updated in the observation window. 2) A recursive solution method was put forward based on the process regression analysis of online LS-SVM, and the inertial measurement units outputs complement with elevator angle variation and environmental factors were chosen as the observed sample sequence. Then the proposed method was used to improve the accuracy and timeliness of the online test model for the inertial navigation system. Finally, the simulation was made when the inertial navigation system has no fault and has lock fault or constant bias fault. The results show that, compared with SVM, LS-SVM, and BP neural network modeling, the proposed method has higher learning speed and robustness performance.