Pyroelectric infrared (PIR) sensors are much promising for device-free localization (DFL) due to their advantages of lower cost, low power consumption, and privacy protection. Most PIR-based localization methods usually assume some geometric models according to the detection principle of PIR sensors, which are however not accurate or robust due to the various cases of infrared radiation from human body, especially the case of multiple persons. Recently, deep learning is utilized in the PIR-based localization method (i.e. PIRNet Yang et al.) and well handles the complex infrared radiation even in the multi-person case. However, this method requires a high training cost, and has very weak generalization ability as it assumes the PIR sensors' deployment in the testing environment is same to the deployment in training environment. To reduce the training cost and achieve high generalization ability, in this paper, we propose a robust method DeepPIRATES, which can be directly utilized in various deployment scenarios without retraining. DeepPIRATES combines deep learning and a geometric model. Specifically, DeepPIRATES divides the localization task into two steps. The first step utilizes a neural network to estimate the azimuth changes of multiple persons to a PIR sensor. Then, DeepPIRATES utilizes the persons' azimuth changes to infer their locations based on a geometric model. Extensive experimental results show that DeepPIRATES can achieve similar localization accuracy as PIRNet, while does not require to be retrained when the sensor deployment changes.

中文翻译：

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DeepPIRATES：一种具有高泛化能力的基于Training-Light PIR的定位方法


热释电红外（PIR）传感器由于其成本较低、功耗低和隐私保护等优点，在无设备定位（DFL）方面前景广阔。大多数基于PIR的定位方法通常根据PIR传感器的检测原理假设一些几何模型，但由于人体红外辐射的情况不同，尤其是多人的情况，这些模型并不准确或鲁棒。最近，深度学习被用于基于 PIR 的定位方法（即 PIRNet Yang 等人），即使在多人情况下也能很好地处理复杂的红外辐射。然而，该方法需要较高的训练成本，并且由于假设PIR传感器在测试环境中的部署与在训练环境中的部署相同，因此泛化能力很弱。为了降低训练成本并实现高泛化能力，在本文中，我们提出了一种鲁棒的方法DeepPIRATES，它可以直接用于各种部署场景而无需重新训练。 DeepPIRATES 结合了深度学习和几何模型。具体来说，DeepPIRATES将本地化任务分为两个步骤。第一步利用神经网络来估计多人相对于 PIR 传感器的方位角变化。然后，DeepPIRATES 利用人的方位角变化，根据几何模型推断他们的位置。大量的实验结果表明，DeepPIRATES 可以实现与 PIRNet 相似的定位精度，同时在传感器部署发生变化时不需要重新训练。