论文标题
通过歧管匹配弱监督的室内定位
Weakly Supervised Indoor Localization via Manifold Matching
论文作者
论文摘要
推断移动设备在室内设置中的位置是一个最重要的开放问题。不需要部署昂贵基础架构的领先方法是指纹识别，在该方法中，分类器经过培训以根据其捕获的信号来预测设备的位置。这种方法的主要警告是，获得足够大，准确的训练套件可能非常昂贵。在这里，我们提出了一种仅需要少量设备的位置的弱监督方法。通过将信号的低维频谱表示与给定的室内环境草图匹配来完成本地化。我们在模拟和真实数据上测试了我们的方法，并表明它的准确性数米，与完全监督的方法相当。我们的方法的简单性及其在最小的监督中的准确性使其非常适合在室内定位系统中实施。
Inferring the location of a mobile device in an indoor setting is an open problem of utmost significance. A leading approach that does not require the deployment of expensive infrastructure is fingerprinting, where a classifier is trained to predict the location of a device based on its captured signal. The main caveat of this approach is that acquiring a sufficiently large and accurate training set may be prohibitively expensive. Here, we propose a weakly supervised method that only requires the location of a small number of devices. The localization is done by matching a low-dimensional spectral representation of the signals to a given sketch of the indoor environment. We test our approach on simulated and real data and show that it yields an accuracy of a few meters, which is on par with fully supervised approaches. The simplicity of our method and its accuracy with minimal supervision makes it ideal for implementation in indoor localization systems.
