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%.

本文提出了一种基于深度学习的融合定位系统，并提出了用于不同分布样本神经网络模型性能改进的迁移学习策略，结果表明在整个楼层场景下，融合网络的平均定位精度为 0.506m。实验结果表明，惯性导航定位步长和不同行人旋转角度的估计精度可平均提高 53.3％，不同设备的蓝牙定位精度可提高 33.4％，融合可提高 31.6％。