改进的自适应卡尔曼滤波在北斗伪距单点定位中的研究

针对加权最小二乘法定位精度不高,卡尔曼滤波对初始位置敏感以及噪声协方差固定不变的缺点,提出了一种将加权 最小二乘法和改进的卡尔曼滤波相结合的伪距单点定位解算方法。 该方法首先利用加权最小二乘法解算出接收机初始位置, 然后将该位置作为改进的自适应卡尔曼滤波的初始值,再建立动力学模型来进行滤波。 实验结果表明基于移动窗口协方差估 计的自适应卡尔曼滤波相比于传统卡尔曼滤波,能将单点定位精度提高 50%,收敛速度也提高了 90%。 该算法可以用在对精 度要求不高的民用导航和定位中。

Research on improved adaptive Kalman filter in Beidou pseudorange single point positioning

In order to solve the defects as low positioning accuracy of weighted least squares method, sensitive to initial position and fixed noise covariance of kalman filter, a pseudo-range single-point positioning method combining weighted least squares and improved Kalman filtering is proposed. This method first uses the weighted least squares to calculate the initial receiver position, then uses this position as the initial value of the improved adaptive Kalman filter, and finally establishes a dynamic model for filtering. Experimental results show that compared with traditional Kalman filtering, adaptive Kalman filtering based on moving window covariance estimation can improve the accuracy of single-point positioning by 50% and the convergence speed by 90%. The algorithm can be used in civil navigation and positioning with little high accuracy requirements.