自适应UKF在北斗伪距定位中的研究

针对北斗伪距定中噪声统计特性未知或者不准确带来的定位精度不高问题,为减小噪声统计特性的不准确在滤波过程中对状态估计带来的影响,采用了无迹卡尔曼滤波(UKF)和噪声统计值估计器相结合的滤波方法,该方法在UKF中引入改进的噪声估计Sage-Husa算法,对系统噪声和观测噪声进行实时估计,抵抗不准确噪声在定位解算时带来的误差;最后在进行状态更新时引入一个收敛因子对每一次滤波状态进行更新,保证算法的收敛性。实验结果表明,该方法与传统的无迹卡尔曼滤波相比,在提升算法收敛速度的同时,将伪距定位的精度提高了40%左右,可用于带有时变噪声和未知噪声的定位系统中。

Research on the Beidou pseudo ranges positioning based on adaptive UKF algorithm

Aiming at the low accuracy of Beidou pseudo ranges that is caused by some noises with unknown reason or inaccurate statistical characteristics, and in order to reduce the influence of state estimation on filtering process because of the inaccuracy of statistical characteristics of noises, a new method of positioning calculation that unscented Kalman filter (UKF) combines with noise statistics estimator is proposed. The method introduces an improved noise estimation algorithm Sage Husa and combines it with UKF, then the algorithm adaptively estimates the noise of the system and the observation in real time, and resists the error caused by inaccurate noise in positioning calculation. Finally, a convergent factor is added to UKF when the algorithm updates the filtering state in order to ensure the convergence of the algorithm. The experiment indicates that the adaptive method increases the precision of pseudo ranges positioning around 40% comparing with the traditional unscented Kalman filter, the method can deal with the influence of inaccurate noise, and the algorithm convergence speed of the algorithm is promoted at the same time, it can be also used in positing system with time varying noise and unknown statistical noise.