有色观测噪声下的自适应 UKF 在北斗多路径误差削弱中的研究

针对由环境复杂性而造成的北斗多路径误差不能有效削弱的问题, 提出了一种基于新的误差模型下的自适应无迹卡 尔曼滤波(UKF)方法。 该方法首先利用量测状态扩增法来解决量测噪声为有色噪声的问题,再用改进的 Sage-Husa 自适应 UKF 来动态估计系统噪声和量测噪声,从而解决噪声统计特性未定造成的误差削弱效果不明显的情况。 实验结果表明在有色 观测噪声下的改进 Sage-Husa 自适应 UKF 算法相比于传统 UKF,能够将多路径误差削弱近 60%,该方法在针对北斗定位中由于 多路径误差产生的噪声不可知的情况具有很强的适用性。

Research on the Beidou multipath error reduction based on adaptive UKF algorithm under colored abservation noise

In order to solve the problem that the Beidou multipath error caused by environmental complexity cannot be effectively eliminated, an adaptive unscented Kalman filter (UKF) method based on a new error model is proposed. Firstly, the method uses the measurement state amplification method to solve the problem that the measurement noise is colored noise, and then uses the improved Sage-Husa adaptive UKF to dynamically estimate the system noise and measurement noise, so as to solve the situation that the error offset effect is not obviously caused by the undetermined statistical characteristics of noise. Comparing this method with the traditional UKF, the experimental results show that the improved Sage-Husa adaptive UKF algorithm under colored observation noise can reduce the multipath error bynearly 60%. This method has strong applicability for the case where the noise generated by the multipath error is unknown to the Beidou positioning.