In order to better distinguish the pedestrian positioning information based on micro inertial measurement unit ( IMU), this paper deeply studies the traditional pedestrian dead reckoning (PDR) algorithm model, and finds that the traditional algorithm has single discrimination conditions, low accuracy and is not suitable for scenes with a variety of terrain. Aiming at the problem of inaccurate step estimation model in traditional algorithms, this study first proposes an error compensation optimization algorithm based on extended Kalman filter (EKF) to realize the error compensation of accelerometer, gyroscope and other sensors integrated in IMU. The study put the optimized original data into BP neural network algorithm to train the single parameter step estimation empirical model. The experimental results show that the step size algorithm based on BP neural network fusion basic model can improve the closed-loop accuracy by more than 0. 3% and reduce the open-loop error by 8. 5 times compared with the simple basic step size model. The improved PDR algorithm based on BP neural network can effectively suppress the error dispersion of inertial algorithm.