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 0.506 meters. 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%.

中文翻译：

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SmartFPS：基于神经网络的无线惯性融合定位系统

目前的融合定位系统主要基于滤波算法，如卡尔曼滤波或粒子滤波。然而，实际应用场景的系统复杂度往往非常高，例如行人惯性导航系统中的噪声建模，或者指纹匹配和定位算法中的环境噪声建模。针对这一问题，本文提出了一种基于深度学习的融合定位系统，并提出了一种迁移学习策略，用于提高神经网络模型对不同分布样本的性能。结果表明，在全楼层场景下，融合网络的平均定位精度为0.506米。