一种改良Kalman滤波参数辨识粗对准方法

摘要

针对传统基于g信息的粗对准的捷联惯导系统中，受传感器噪声的影响，存在效视运动无法提取和双向量共线的缺点，提出了一种基于改良 Kalman 滤波的参数辨识粗对准方法。该方法通过构建视在重力在初始载体系中的映射模型，利用改良 Kalman 滤波进行模型参数辨识，然后通过识别参数重新构建视在重力在初始载体系中的映射，解决了由于传感器噪声导致有效视运动无法正常提取的缺点。利用识别参数具有随估计次数增多得到优化的特点，构造初始时刻和最终时刻向量，避免双向量共线问题。利用改良 Kalman 滤波算法的自适应特点，优化参数识别精度与速度。转台实验表明，采用改良Kalman滤波方法航向对准精度为-0.0414°，标准差为0.041°，而传统RLS方法得到的航向精度为-0.0738°，标准差为0.128°。由此可知，本文提出的方法性能更优。%Traditionalg-based coarse alignment in SINS has two problems under the influences of sensor noises. The first one is that the effective gravitational apparent motion information is difficult to extract, and the second is how to avoid the collinear problem between two vectors. To solve these two problems, a new algorithm based on an improved Kalman filter is proposed. This method reconstructs the gravitational vectors based on parameter identification method, so the stochastic noises in accelerometer measurements are reduced. Since the parameters are constants, it can avoid the collinear by the reconstructed vectors. The improved Kalman filtering algorithm is adaptive, and it does not need the statistical information of the accelerometer measurements. Turntable tests show that the yaw error and the standard deviation of the new method are -0.0414° and 0.041° respectively, while the yaw error and the standard deviation of the recursive least-squares method is -0.0738° and 0.041° respectively, showing that the new method has better performance.