We propose and implement a novel indoor localization scheme, Swadloon, built upon an accurate acoustic direction finding. Swadloon leverages sensors of the smartphone without the requirement of any specialized devices. The scheme Swadloon does not rely on any fingerprints and is very easy to use: a user only needs to shake the phone for a short duration before walking and localization. Our Swadloon design exploits a key observation: the relative shift and velocity of the phone-shaking movement corresponds to the subtle phase and frequency shift of the Doppler effects experienced in the received acoustic signal by the phone. A novel method is designed to derive the direction from the phone to the acoustic source by combining the velocity calculated from the subtle Doppler shift with the one from the inertial sensors of the phone. Then a real-time precise localization and tracking is enabled by using a few anchor speakers with known locations. Major challenges in implementing Swadloon are to measure the frequency shift precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, then design and deploy Swadloon in several floors of an indoor building each with area about 2000m^2. Our extensive experiments show that the mean error of direction finding is around 2.1 degree when the acoustic source is within the range of 32m. For indoor localization, the 90-percentile errors are under 0.92m, while the maximum error is 1.73m and the mean is about 0.5m. For real-time tracking, the errors are within 0.4m for walks of 51m.
基于信道状态信息（Channel State Information，CSI）的指纹识别技术在WIFI室内定位中得到了广泛的关注，频率多样且时间稳定的CSI比粗接收信号强度（Recovered Signal Strength，RSS）更能反映位置相关的信道特性。然而，CSI的获取需要接入点（AP）的合作并且仅涉及多个帧，这对现实世界的部署施加了限制。在本文中，我们提出了CRISLoc，第一个CSI指纹定位原型系统使用无处不在的智能手机。CRISLoc在一个完全被动的模式下运行，在他自己的CSI采集过程中偷听数据包。通过校准WiFi放大器电路施加的失真来净化智能手机CSI。CRISLoc通过联合聚类和离群值检测方法来解决改变AP的挑战。提出了一种新的迁移学习方法，在过时的指纹和一些新的测量的基础上重建高维CSI指纹数据库，并提出了一种增强的KNN方法来精确定位智能手机的位置。我们的研究揭示了智能手机CSI的稳定性和灵敏度的重要属性，这是以前没有报道过的。实验结果表明，CRISLoc在6m × 8m的研究实验室中可以实现约0.29m的平均误差。移动一个和两个AP后，平均误差分别增加了5.4 cm和8.6 cm，这验证了CRISLoc对环境变化的鲁棒性。
Pible: Battery-Free Mote for Perpetual Indoor BLE Applications
University of California, San Diego（加州大学圣迭戈分校）；University of California, Los Angeles（加州大学洛杉矶分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Bologna（博洛尼亚大学）；ETH Zurich（苏黎世联邦理工学院）
