论文标题
用于室内定位的多模式复发融合
Multi-Modal Recurrent Fusion for Indoor Localization
论文作者
论文摘要
本文使用多模式无线信号（包括Wi-Fi，惯性测量单元（IMU）和Ultra Wideband（UWB））考虑了室内定位。通过将定位作为多模式序列回归问题，提出了一种多流式复发方法，以在复发性神经网络的背景下将每种模态的当前隐藏状态结合在一起，同时考虑了直接从其自身近期的州中直接学到的模态不确定性。对所提出的方法进行了对大规模SPAWC2021多模式定位数据集进行评估，并将其与包括三材料方法，传统指纹方法和基于卷积网络的方法在内的广泛基线方法进行了比较。
This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of each modality in the context of recurrent neural networks while accounting for the modality uncertainty which is directly learned from its own immediate past states. The proposed method was evaluated on the large-scale SPAWC2021 multi-modal localization dataset and compared with a wide range of baseline methods including the trilateration method, traditional fingerprinting methods, and convolution network-based methods.
