一种RVM的INS参数长期稳定性预测与补偿方法

为提高惯导系统参数长期稳定性,降低人工标定成本,增强惯导系统使用效能,提出一种基于相关向量机的惯导系统参数长期稳定性预测和补偿方法,选择均值和标准差作为参数稳定性的性能指标.而对于均值随时间变化具有明显规律的参数,采用RVM方法对存贮时间较长的参数稳定性均值进行回归建模,根据模型对存贮时间较短的参数稳定性进行性能预测和标定参数补偿．最后对惯导系统中重要参数加速度计标度因数长期稳定性进行建模预测和参数补偿,补偿后结果显示,间隔时间约6个月的参数稳定性均值性能提高了50.90％,验证了所提方法具有很好的实际应用价值,且表明使用该方法能够代替人工标定,以增强惯导系统使用效能．

RVM-based prediction and compensation method for the long-term stability of INS system parameters

To improve the long-term stability performance, reduce the manual calibration costs and enhance the use efficiency of inertial navigation systems, we propose a prediction and compensation method for the long-term stability of inertial navigation system parameter based on correlation vector machine, in which we choose the mean and standard deviation as the performance indicators. For the mean parameter with a significant change with time in the law, we establish regression modeling for longer storage stability parameter by RVM method, and carry out the performance prediction and calibration parameters compensation for the parameters stability of the shorter storage. The paper presents the modeling forecasting and parameter compensation for the long-term stability of the accelerometer scale factor of the important parameters in the inertial navigation system, the parameter stability mean performance for an interval time of 6 months has improved by 50.90%. This result implies that this method can replace the manual calibration and the use efficiency of inertial navigation systems, and verifies the effectiveness of the proposed method.