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.