基于遗忘PI自适应滤波在车载导航中的应用

Application of Adaptive Filtering Based on Forgetting PI in Car Navigation

在INS/GPS车载组合导航系统过程中,存在量测数据出现异常值的问题,而导致结果出现较大的误差及其滤波精度下降的情况。为进一步提高导航系统的精度,提出了一种基于具有遗忘因子的比例积分(proportional integrator, PI)自适应滤波算法来抑制滤波发散的问题。上述算法通过利用新息协方差估计值和量测值实时计算出的量测噪声方差阵,以及引入遗忘因子来增加系统噪声和量测噪声的权重,从而来实现抑制滤波的干扰,采用PI控制算法对观测协方差矩阵可以进行在线修正,从而处理由噪声估计不准而导致滤波发散的问题,来达到提高导航精度的目的。试验结果表明,改进后的算法能有效的抑制滤波发散,比常规的自适应卡尔曼滤波的效果更好,导航的精度提高,同时系统的稳定性也有所提高。

In the process of the INS/GPS vehicle integrated navigation system,there is a problem of abnormal measurement data values,which leads to a large error in the result and a decrease in filtering accuracy.In order to further improve the accuracy of the navigation system,an adaptive filtering algorithm based on proportional integrator(PI)with forgetting factor is proposed to suppress the problem of filtering divergence.The algorithm uses the variance matrix of the measurement noise calculated in real-time by the estimated value of the innovation covariance and the measurement value,and introduces a forgetting factor to increase the weight of the system noise and the measurement noise,so as to suppress the interference of the filter,and adopts PI The control algorithm can modify the observation covariance matrix online,so as to deal with the problem of filter divergence caused by inaccurate noise estimation and achieve the goal of improving navigation accuracy.The experimental results show that the improved algorithm can ef-fectively suppress the filter divergence,which is better than the conventional adaptive Kalman filter,the accuracy of navigation is improved,and the stability of the system is also improved.