Aiming at the problem that the positioning accuracy of satellite navigation system in indoor closed places is too low due to insufficient positioning penetration and the traditional inertial guidance has a large trajectory offset in indoor pedestrian positioning, through the in-depth analysis of pedestrian trajectory projection algorithm (PDR), an improved PDR algorithm is proposed, which aims to improve the positioning accuracy in indoor positioning. The algorithm firstly designs Kalman filter and FIR filter to preprocess the sensor data to improve the data smoothing and anti-noise performance; secondly, it improves the traditional Weinberg step calculation model by adding new variables as the joint reference of step frequency detection and step calculation, which helps to reduce the cumulative error of the step estimation; and then it takes the appropriate threshold value as the pedestrian zero-speed judgment to correct the step number as well as the pedestrian’s position; finally, an extended Kalman filter (EKF) is designed to optimize the pedestrian’s position to achieve the dynamic optimization of the actual pedestrian trajectory. The simulation results show that the improved PDR algorithm significantly improves the positioning accuracy, and the average error of the pedestrian trajectory is reduced from 5.5 m to 1.2 m. Overall, the improved PDR algorithm can effectively reduce the trajectory deviation and the cumulative error, and improve the accuracy of the pedestrian positioning, which is of wide application prospect.