Research on Bluetooth/MIMU Integrated Navigation Algorithm Based on Factor Graph Optimization

With the continuous improvement of human demand for indoor location services, various indoor navigation and positioning methods such as pedestrian dead reckoning and low-power Bluetooth (BLE) are widely used in indoor positioning due to their advantages of low cost, low power consumption and easy portability. However, the positioning accuracy of the Pedestrian Dead Reckoning (PDR) algorithm is affected by the error of the micro inertial measurement unit, and the cumulative error of its positioning is large, and the Bluetooth positioning bounce is serious. Therefore, taking Bluetooth and micro-inertial navigation as the research object, the advantages of Bluetooth positioning technology and pedestrian dead reckoning technology are complementary, and the research on Bluetooth/micro-inertial navigation high-precision indoor fusion positioning technology based on factor graph optimization (FGO) is carried out. By analyzing the error characteristics of the Bluetooth module and the inertial sensor in the indoor environment, an integrated navigation and positioning method based on the combination of AOA ranging and PDR is designed, a Gauss Newton algorithm based on Brayden-Fletcher-Goldfarb-Shanno (BFGS) is proposed to ensure that the Hessian matrix remains positive during the iteration process, and the experimental results show that compared with the single navigation mode, the positioning accuracy of the BLE/PDR information fusion algorithm based on factor graph optimization is higher, and the positioning stability of the model is better.