论文标题
关于基于多输出高斯工艺的大型建筑物中室内定位的指纹数据的多维增强
On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process
论文作者
论文摘要
Wi-Fi指纹识别成为大型室内定位的主要解决方案，因为它的主要优势是不需要新的基础设施和专用设备。但是，在离线阶段测量RSSI等定位指纹的参考点（RP）的数量和分布极大地影响了定位精度；例如，已知Ujiindoorloc在建筑物和地板上具有不均匀的RPS空间分布的问题。已经提出了数据增强作为可行解决方案，不仅可以改善现有指纹数据库中RPS较小的RPS分布，还可以降低构建新指纹数据库的人工和时间成本。 In this paper, we propose the multidimensional augmentation of fingerprint data for indoor localization in a large-scale building complex based on Multi-Output Gaussian Process (MOGP) and systematically investigate the impact of augmentation ratio as well as MOGP kernel functions and models with their hyperparameters on the performance of indoor localization using the UJIIndoorLoc database and the state-of-the-art neural network基于层次RNN的室内定位模型。基于实验结果的研究表明，我们可以通过拟议的基于MOGP的多维数据增强来生成综合RSSI指纹数据，最多是10倍（即10的增强率），即10的增强率，而无需显着影响室内定位的原始效果，与原始数据相比，该数据扩展了范围的范围，并且可以在其中范围覆盖范围。测试数据集的。
Wi-Fi fingerprinting becomes a dominant solution for large-scale indoor localization due to its major advantage of not requiring new infrastructure and dedicated devices. The number and the distribution of Reference Points (RPs) for the measurement of localization fingerprints like RSSI during the offline phase, however, greatly affects the localization accuracy; for instance, the UJIIndoorLoc is known to have the issue of uneven spatial distribution of RPs over buildings and floors. Data augmentation has been proposed as a feasible solution to not only improve the smaller number and the uneven distribution of RPs in the existing fingerprint databases but also reduce the labor and time costs of constructing new fingerprint databases. In this paper, we propose the multidimensional augmentation of fingerprint data for indoor localization in a large-scale building complex based on Multi-Output Gaussian Process (MOGP) and systematically investigate the impact of augmentation ratio as well as MOGP kernel functions and models with their hyperparameters on the performance of indoor localization using the UJIIndoorLoc database and the state-of-the-art neural network indoor localization model based on a hierarchical RNN. The investigation based on experimental results suggests that we can generate synthetic RSSI fingerprint data up to ten times the original data -- i.e., the augmentation ratio of 10 -- through the proposed multidimensional MOGP-based data augmentation without significantly affecting the indoor localization performance compared to that of the original data alone, which extends the spatial coverage of the combined RPs and thereby could improve the localization performance at the locations that are not part of the test dataset.
