SmartFPS: Neural network based wireless-inertial fusion positioning system - Hua, Luchi; Zhuang, Yuan; Yang, Jun (Frontiers in Neurorobotics, 2023)
The current fusion positioning systems are mainly based on filtering algorithms, such as Kalman filter or particle filter. However, these methods often perform poorly in highly nonlinear system and under non-Gaussian noise, and require proper modelling of the systems. The system complexity of practical positioning scenarios is often very high, such as noise modelling in pedestrian inertial navigation systems, or environmental noise modelling for fingerprinting algorithms. To solve this problem, this paper proposes a fusion positioning system based on deep learning, along with a transfer learning strategy for improving the performance of neural network models for samples with different distributions. Verified by Bluetooth-inertial positioning in a whole floor scenario, the mean positioning error of the fusion network was 0.506m. The proposed transfer learning method improved the accuracy of the step length and rotation angle of different pedestrians by 53.3%, the Bluetooth positioning accuracy of different devices by 33.4%, and average positioning error of the fusion system by 31.6%.