基于混合CNN-LSTM与CSI指纹图的室内行人定位
Hybrid CNN-LSTM based Indoor Pedestrian Localization with CSI Fingerprint Maps
AI总结：
本文提出一种基于CSI指纹图和混合CNN-LSTM架构的室内行人定位方法，结合粒子滤波筛选轨迹假设，在动态和静态环境中分别取得0.36米和0.17米的平均RMSE，验证了稀疏观测和有限基础设施条件下细粒度Wi-Fi定位的可行性。
摘要：
本文提出了一种新颖的Wi-Fi指纹定位系统，利用信道状态信息（CSI）数据进行细粒度行人定位。所提出的系统利用从CSI数据中提取特征的频率多样性和空间多样性，生成一种称为CSI指纹图的2D+通道图像。随后，我们利用这种CSI数据的CSI指纹图表示，通过结合卷积神经网络和长短期记忆循环神经网络模型的混合架构生成行人轨迹假设。所提出的架构利用了在邻近位置采集的CSI数据观测之间的时间和空间关系信息。然后采用粒子滤波器分离出最符合人类行走模型的假设。我们将所提方法的实验性能与现有深度学习定位方法（如ConFi、DeepFi）以及自行开发的基于时间特征的LSTM位置分类器进行了比较。实验结果表明性能显著提升，在中等动态环境中平均RMSE为0.36米，在静态环境中为0.17米。我们的方法本质上是一个概念验证，表明在（1）观测稀疏可用、（2）基础设施需求有限、（3）训练和测试环境中存在中等程度的短期和长期噪声的情况下，可靠的细粒度基于Wi-Fi的行人定位是一种潜在可行的选择。
英文摘要：
The paper presents a novel Wi-Fi fingerprinting system that uses Channel State Information (CSI) data for fine-grained pedestrian localization. The proposed system exploits the frequency diversity and spatial diversity of the features extracted from CSI data to generate a 2D+channel image termed as a CSI Fingerprint Map. We then use this CSI Fingerprint Map representation of CSI data to generate a pedestrian trajectory hypothesis using a hybrid architecture that combines a Convolutional Neural Network and a Long Short-Term Memory Recurrent Neural Network model. The proposed architecture exploits the temporal and spatial relationship information among the CSI data observations gathered at neighboring locations. A particle filter is then employed to separate out the most likely hypothesis matching a human walk model. The experimental performance of our method is compared to existing deep learning localization methods such ConFi, DeepFi and to a self-developed temporal-feature based LSTM based location classifier. The experimental results show marked improvement with an average RMSE of 0.36 m in a moderately dynamic and 0.17 m in a static environment. Our method is essentially a proof of concept that with (1) sparse availability of observations, (2) limited infrastructure requirements, (3) moderate level of short-term and long-term noise in the training and testing environment, reliable fine-grained Wi-Fi based pedestrian localization is a potential option.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
