The smart-phone-based personnel indoor positioning is fragile to the phone attitude. To address this issue, an indoor positioning method integrating WiFi and the wearable inertial navigation module is proposed. The pedestrian dead reckoning (PDR) positioning is achieved by leveraging the wearable inertial navigation module fixed to the chest. And the influence from the smartphone attitude is avoided. WiFi fingerprint positioning is also adopted by using the proposed weighted Bayesian algorithm, which provides the initial position for PDR positioning. Meanwhile, the WiFi positioning are continuously fused with PDR positioning under the framework of the unscented Kalman filter to reduce the cumulative positioning error of pure PDR positioning. Finally, a large number of experiments are implemented in the real indoor environment. Compared with the traditional Bayesian algorithm, experimental results show that the positioning error achieved by the proposed weighted Bayesian WiFi positioning algorithm is reduced by 51. 9% . The proposed positioning method integrating WiFi and the wearable inertial navigation module has better accuracy and stability. Compared with the pure PDR positioning algorithm, the average positioning error is reduced by 65. 2% . Furthermore, compared with implementing the same algorithm on the smart phone, the average positioning errors under three different phone attitudes are reduced by 12. 3% , 39. 3% and 48. 4% , respectively.