SmartFPS: Neural Network based Wireless-inertial fusion positioning system
This work addresses fusion positioning for pedestrian navigation, offering incremental improvements in accuracy through transfer learning.
The paper tackled the problem of high complexity in fusion positioning systems by proposing a deep learning-based system with a transfer learning strategy, achieving an average positioning accuracy of 0.506m in a floor scenario and improving inertial navigation and Bluetooth positioning accuracies by up to 53.3%.
The current fusion positioning systems are mainly based on filtering algorithms, such as Kalman filtering or particle filtering. However, the system complexity of practical application scenarios is often very high, such as noise modeling in pedestrian inertial navigation systems, or environmental noise modeling in fingerprint matching and localization algorithms. To solve this problem, this paper proposes a fusion positioning system based on deep learning and proposes a transfer learning strategy for improving the performance of neural network models for samples with different distributions. The results show that in the whole floor scenario, the average positioning accuracy of the fusion network is 0.506m. The experiment results of transfer learning show that the estimation accuracy of the inertial navigation positioning step size and rotation angle of different pedestrians can be improved by 53.3% on average, the Bluetooth positioning accuracy of different devices can be improved by 33.4%, and the fusion can be improved by 31.6%.