Learning-based Techniques for Transmitter Localization: A Case Study on Model Robustness
Learning-based Techniques for Transmitter Localization: A Case Study on Model Robustness
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DOI：
10.1109/secon58729.2023.10287483
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发表时间：
2023-09
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影响因子：
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通讯作者：
Frost Mitchell;Neal Patwari;Aditya Bhaskara;S. Kasera
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文献类型：
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作者：
Frost Mitchell;Neal Patwari;Aditya Bhaskara;S. Kasera
在大型户外环境中，发射机定位仍然是一个具有挑战性的问题，特别是当发射机和接收机被允许移动时。我们在无线电动态区（RDZ）的背景下考虑本地化，RDZ是一个提议的实验平台，研究人员可以在其中部署实验设备，波形或无线网络。必须保护RDZ外的无线用户免受RDZ内源的有害干扰。在这种情况下，对造成干扰的发射机进行定位至关重要。开发数据驱动的定位方法的一个显著障碍是缺乏大规模的训练数据集。作为我们的第一个贡献，我们提出了一个新的定位数据集，在4平方公里内以462.7 MHz的频率捕获。拥有29个不同的接收器和超过4500个独特的发射机位置。接收器既有移动的，也有固定的，并且在硬件、放置和增益设置方面都是异构的。接下来，我们提出了一种新的基于机器学习的定位方法，可以处理来自未校准的异构接收器的输入。最后，我们利用我们的新数据集来研究我们的技术和其他技术对大多数实际应用中常见的“非分布”（OOD）输入的鲁棒性。我们的技术，CUTL（校准U-Net发射机定位），在分布数据上的准确性提高了49%，并且比以前的方法在OOD数据上的鲁棒性更强。
Transmitter localization remains a challenging problem in large-scale outdoor environments, especially when transmitters and receivers are allowed to be mobile. We consider localization in the context of a Radio Dynamic Zone (RDZ), a proposed experimental platform where researchers can deploy experimental devices, waveforms, or wireless networks. Wireless users outside an RDZ must be protected from harmful interference coming from sources inside the RDZ. In this setting, localizing transmitters that are causing interference is critical. One notable obstacle for developing data-driven methods for localization is the lack of large-scale training datasets. As our first contribution, we present a new dataset for localization, captured at 462.7 MHz in a 4 sq. km outdoor area with 29 different receivers and over 4,500 unique transmitter locations. Receivers are both mobile and stationary, and heterogeneous in terms of hardware, placement, and gain settings. Next, we propose a new machine learning-based localization method that can handle inputs from uncalibrated, heterogeneous receivers. Finally, we leverage our new dataset to study the robustness of our technique and others against “out of distribution” (OOD) inputs that are common in most real life applications. We show that our technique, CUTL (Calibrated U-Net Transmitter Localization), is 49% more accurate on in-distribution data, and more robust than previous methods on OOD data.