With the increasing demand of indoor location-based services, such as tracking targets in a smart building, device-free localization technique has attracted great attentions because it can locate the targets without employing any attached devices. Due to the limited space and complexity of the indoor environment, there still exist challenges in terms of high localization accuracy and high efficiency for indoor localization. In this paper, for addressing such issues, we first convert the received signal strength (RSS) signals into image pixels. The localization problem is then formulated as an image classification problem. To well handle the variant RSS images, a deep convolutional neural network is then structured for classification. Finally, for validating the proposed scheme, two real testbeds are built in the indoor environments, including a living room and a corridor of an apartment. Experimental results show that the proposed scheme achieves good localization performance. For example, the localization accuracy can reach up to 100% in the scenario of living room and 97.6% in the corridor. Moreover, the proposed approach outperforms the methods of the K-nearest-neighbor and the support vector machines in both the noiseless and noisy environments.

中文翻译：

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使用 DCNN 实现基于位置的服务的室内无设备被动定位

随着智能建筑中跟踪目标等室内定位服务需求的不断增加，无设备定位技术因其无需任何附加设备即可定位目标而备受关注。由于室内环境的空间有限和复杂性，室内定位的高定位精度和高效率仍然存在挑战。在本文中，为了解决这些问题，我们首先将接收到的信号强度 (RSS) 信号转换为图像像素。然后将定位问题表述为图像分类问题。为了很好地处理变体 RSS 图像，然后构建了一个深度卷积神经网络以进行分类。最后，为了验证所提出的方案，在室内环境中建立了两个真实的测试台，包括客厅和公寓的走廊。实验结果表明，所提出的方案取得了良好的定位性能。比如客厅场景定位准确率可达100%，走廊场景定位准确率可达97.6%。此外，所提出的方法在无噪声和有噪声的环境中都优于 K 最近邻和支持向量机的方法。