MobLoc: CSI-Based Location Fingerprinting With MUSIC
MobLoc: CSI-Based Location Fingerprinting With MUSIC
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DOI：
10.1109/jispin.2023.3336609
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Stepan Mazokha;Fanchen Bao;G. Sklivanitis;Jason O. Hallstrom
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文献类型：
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作者：
Stepan Mazokha;Fanchen Bao;G. Sklivanitis;Jason O. Hallstrom
在过去的十年中，已经提出了许多基于CSI的定位方法。指纹识别已经成为最高成就的方法之一，因为它能够捕获环境特征，而这些特征是使用经典的定位机制（例如，定位）不容易捕获的。然而，通常所提出的方法受到依赖于大规模训练数据集的限制。此外，方法很少在非固定设备上进行评估，这在现实世界环境中最常见。在我们的工作中，我们通过引入MobLoc来应对这些挑战。我们采用基于MUSIC伪谱的指纹识别，它可以受益于，但不严重依赖于大量的数据包为每个指纹。为了评估我们的方法，我们利用被动收集的CSI测量的公开可用数据集DLoc（Ayyalasomayajula等人，2020），其中发射器在运动中发送信号。我们还将MobLoc与一系列最先进的本地化方法进行基准测试。结果表明，我们的方法优于SpotFi（Kotaru等人，2015）、EntLoc（Chen等人，2019）和AngLo（Chen et al.，2020），并且福尔斯非常难以实现DLoc精度。在DLoc数据集上，MobLoc在简单环境中实现了0.33 m（和0.82 m，第90百分位数）的定位误差，在复杂环境中实现了1.15 m（2.59 m，第90百分位数）的定位误差。然而，尽管MobLoc不超过DLoc的准确性，我们认为它的性能作为在现实世界的环境中部署该方法所需的计算资源的权衡。我们预计，这一优势将使MobLoc在城市景观定位系统，计算资源的成本是关键。
Many CSI-based localization methods have been proposed over the last decade. Fingerprinting has been one of the highest achieving approaches due to its capacity to capture environmental characteristics that are not readily captured using classic localization mechanisms such as multilateration. However, oftentimes the proposed methods are limited by reliance on large-scale training datasets. Further, methods are rarely evaluated on nonstationary devices, which are the most common in real-world environments. In our work, we address these challenges by introducing MobLoc. We adopt MUSIC pseudospectrum-based fingerprinting, which can benefit from, but does not heavily rely upon a large number of packets for each fingerprint. To evaluate our method, we leverage a publicly available dataset of passively collected CSI measurements, DLoc (Ayyalasomayajula et al., 2020), where an emitter sends signals in motion. We also benchmark MobLoc against a series of state-of-the-art localization methods. The results demonstrate that our method outperforms SpotFi (Kotaru et al., 2015), EntLoc (Chen et al., 2019), and AngLo (Chen et al., 2020), and falls very short of achieving DLoc accuracy. On the DLoc dataset, MobLoc achieves 0.33 m median (and 0.82 m, 90th percentile) localization error in a simple environment and 1.15 m median (2.59 m, 90th percentile) localization error in a complex environment. However, despite MobLoc not exceeding DLoc's accuracy, we consider its performance as a tradeoff for computational resources required to deploy the method in a real-world environment. We anticipate that this advantage will enable the adoption of MobLoc in city-scape localization systems, where the cost of computational resources is key.