Group Wi-Lo: Maintaining Wi-Fi-based Indoor Localization Accurate via Group-wise Total Variation Regularization
Group Wi-Lo: Maintaining Wi-Fi-based Indoor Localization Accurate via Group-wise Total Variation Regularization
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DOI：
10.1109/ipin.2019.8911754
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Masato Sugasaki;K. Tsubouchi;M. Shimosaka;N. Nishio
中科院分区：
文献类型：
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作者：
Masato Sugasaki;K. Tsubouchi;M. Shimosaka;N. Nishio
众所周知，基于Wi-Fi指纹的定位在室内定位技术中占有重要地位；然而，由于信号强度随时间的分布漂移，它仍然对其长期使用的可持续性性能构成挑战。因此，对指纹进行繁重的连续测量是不可避免的。本文提出了一种基于机器学习的方法，通过高效的增量学习(再训练)来解决普通Wi-Fi指纹定位维护成本过高的问题。具体地说，我们提出了一种全新的再训练方法，称为GroupWi-Lo，该方法专注于最小化针对指纹增量调查(即校准)的参数变化。我们的方法试图保持先前训练的模型的参数不变，同时最小化在上次调查中获得的数据集的误差。这种公式有助于保持针对每个调查的数据集的有限大小的过拟合的稳健性。在实验室和非受控环境下的实验结果表明，GroupWi-Lo的计算性能与现有的半监督方法和标准增量训练方法相比具有相当的性能，但其计算代价与调查次数无关。
Wi-Fi fingerprint-based localization is known to be prominent for indoor positioning technology; however, it is still challenging on sustainability of its performance for long-term use due to distribution drifts of the signal strength across time. Therefore, the laborious continual surveys on fingerprint are inevitable. In this paper, we propose a new scheme for solving the large cost of maintaining common Wi-Fi fingerprint-based localization with machine-learning-based way by efficient incremental learning (retraining). Specifically, we propose a brand new retraining method, called GroupWi-Lo, that focuses on minimization of parameter variation with respect to the incremental surveys on fingerprint (i.e., calibration). Our method tries to keep the parameters of the previously trained model unchanged while minimizing the error on the dataset obtained in the last surveys. This formulation is helpful to keep robustness against overfitting from the limited size of the dataset per survey. The experimental results both in the lab and the uncontrolled environment show that GroupWi-Lo achieves competitive performance among the state-of-the-art methods, while its computational cost retains independent of the number of surveys compared with existing the semi-supervised approach and standard incremental training approach.