More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet of Things ecosystem, low-power Bluetooth technology provides a new direction for indoor positioning. Most existing indoor positioning algorithms are applied to specific situations. Thus, they are difficult to adapt to actually complex environments and different users. To solve this problem, this paper proposes a precise dead reckoning algorithm based on Bluetooth and multiple sensors (DRBMs). To address positioning accuracy, this paper improves the traditional Bluetooth propagation model and calculate the steps and step lengths for different users in the process of multisensor track calculation. In addition, this paper fuses the localization results of Bluetooth propagation model and multiple sensors through the Kalman filter. The experiment results show that the proposed DRBM algorithm can obtain accurate positions. The localization accuracy is within 1 m, and the best can be controlled within 0.5 m. Compared with the traditional Bluetooth positioning methods and the traditional dead reckoning methods, the proposed algorithm greatly improves positioning accuracy and universality.