Geomagnetism is promising for indoor localization due to its omnipresence, high stability, and availability of magnetometers in smartphones. Previous works often fuse it with pedometer via particles, which are computationally intensive and make strong user behavior assumptions. To overcome that, we propose Magil, an approach leveraging geo mag netism for i ndoor l ocalization. To our best knowledge, this is the first piece of work using geomagnetism for smartphone localization without the need of pedometer or user walking model. Magil is applicable to any open or complex indoor environment. In the offline phase, Magil collects and stores geomagnetic fingerprints while surveyors walk indoors. In the online phase, it employs a fast algorithm to match the geomagnetic variation of the target with the stored fingerprints. Given closely matched segments, Magil constructs user trajectory efficiently with a modified shortest path formulation by selecting and connecting these matched segments. To further improve accuracy and deployability, we propose MagFi, which extends Mag il by fusing it with Wi- Fi . MagFi further collects opportunistic Wi-Fi RSSI for fingerprint construction. We have implemented both Magil and MagFi and conducted extensive experiments in our campus. Results show that our schemes outperform state-of-the-art schemes by a wide margin (often cutting localization error by 30%).

中文翻译：

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基于地磁的高效室内定位

地磁学因其无所不在、高稳定性和智能手机中磁力计的可用性而有望用于室内定位。以前的作品经常通过粒子将其与计步器融合在一起，这是计算密集型的，并且会做出强烈的用户行为假设。为了克服这个问题，我们提出了 Magil，一种利用地理的方法杂志网络主义一世室内l定位。据我们所知，这是第一个使用地磁进行智能手机定位的工作，无需计步器或用户步行模型。Magil 适用于任何开放或复杂的室内环境。在离线阶段，Magil 收集和存储地磁指纹，而测量员则在室内行走。在在线阶段，它采用快速算法将目标的地磁变化与存储的指纹进行匹配。给定紧密匹配的段，Magil 通过选择和连接这些匹配的段，使用修改后的最短路径公式有效地构建用户轨迹。为了进一步提高准确性和可部署性，我们提出了 MagFi，它扩展了麦格il 通过将其与 Wi- 融合Fi. MagFi 进一步收集机会主义 Wi-Fi RSSI 用于指纹构建。我们已经实施了 Magil 和 MagFi，并在我们的校园内进行了广泛的实验。结果表明，我们的方案大大优于最先进的方案（通常将定位误差降低 30%）。