The position and heading error cannot be effectively corrected in the shoe mounted indoor pedestrian navigation and positioning system with the inertial sensors as the core. To address this issue, an indoor fusion positioning method based on Kalman particle filter with cascade structure is proposed, which integrates the micro electro mechanical system (MEMS) inertial sensor and indoor building structure information. Firstly, the zero-velocity update is used in the lower Kalman filter to initially correct the inertial navigation solution error. Then, the upper particle filter utilizes indoor building structure information to further calibrate pedestrian position and heading through wall detection. Experimental results show that the building structure information / inertial navigation pedestrian navigation algorithm based on the cascade filter can effectively reduce the accumulation of inertial conductivity error. The “wall-crossing” behavior of pedestrian trajectory caused by the unobservability of course can be corrected when the traditional algorithm adopts zero velocity update and correction system error. This method can reduce the error in the process of location update. Compared with the single inertial pedestrian positioning, the root mean square error of location is reduced from 0. 69 m to 0. 39 m, and the heading of the root mean square error is reduced from 0. 81° to 0. 72°.