论文标题
SmartFP：基于神经网络的无线惯性融合定位系统
SmartFPS: Neural Network based Wireless-inertial fusion positioning system
论文作者
论文摘要
当前的融合定位系统主要基于过滤算法，例如卡尔曼过滤或粒子过滤。但是，实际应用程序场景的系统复杂性通常很高，例如行人惯性导航系统中的噪声建模或指纹匹配和本地化算法中的环境噪声建模。为了解决这个问题，本文提出了一个基于深度学习的融合定位系统，并提出了一种转移学习策略，以改善具有不同分布的样本的神经网络模型的性能。结果表明，在整个地板方案中，融合网络的平均定位精度为0.506m。转移学习的实验结果表明，惯性导航定位步长的估计精度和不同行人的旋转角平均可以提高53.3％，不同设备的蓝牙定位精度可以提高33.4％，并且融合可以提高33.4％。
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%.
