[论文解读] An iBeacon based Proximity and Indoor Localization System

Faheem Zafari, Ioannis Papapanagiotou+2 more|arXiv (Cornell University)|Mar 22, 2017

Indoor and Outdoor Localization Technologies参考文献 8被引用 51

一句话总结

该论文通过引入 proximity SRA 和 SKF 来实现服务端近距离感知和室内定位的 iBeacons，并提出级联 Kalman Filter-Particle Filter (KFPF) 用于定位，在相较于标准 iBeacon 方法上取得显著的精度提升。

ABSTRACT

Indoor localization and Location Based Services (LBS) can greatly benefit from the widescale proliferation of communication devices. The basic requirements of a system that can provide the aforementioned services are energy efficiency, scalability, lower costs, wide reception range, high localization accuracy and availability. Different technologies such as WiFi, UWB, RFID have been leveraged to provide LBS and Proximity Based Services (PBS), however they do not meet the aforementioned requirements. Apple's Bluetooth Low Energy (BLE) based iBeacon solution primarily intends to provide Proximity Based Services (PBS). However, it suffers from poor proximity detection accuracy due to its reliance on Received Signal Strength Indicator (RSSI) that is prone to multipath fading and drastic fluctuations in the indoor environment. Therefore, in this paper, we present our iBeacon based accurate proximity and indoor localization system. Our two algorithms Server-Side Running Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximity detection accuracy of iBeacons by 29% and 32% respectively, when compared with Apple's current moving average based approach. We also present our novel cascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoor localization. Our cascaded filter approach uses a Kalman Filter (KF) to reduce the RSSI fluctuation and then inputs the filtered RSSI values into a Particle Filter (PF) to improve the accuracy of indoor localization. Our experimental results, obtained through experiments in a space replicating real-world scenario, show that our cascaded filter approach outperforms the use of only PF by 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environments respectively, and achieves a localization error as low as 0.70 meters in 2D environment and 0.947 meters in 3D environment.

研究动机与目标

• 提升 iBeacons 的近距离检测准确性，超越基于 RSSI 的滑动平均。
• 在 RSSI 波动存在的情况下，使用 iBeacons 实现准确的室内定位。
• 将计算卸载到服务器以降低用户设备的能源消耗。
• 开发并验证在二维和三维室内环境中工作的算法。

提出的方法

• 开发服务器端运行平均（SRA），通过特定站点的路径损耗模型将 RSSI 映射到距离并对近距离区域进行分类。
• 开发服务器端卡尔曼滤波器（SKF），在距离转换前对 RSSI 及其变化率应用卡尔曼滤波以平滑 RSSI。
• 引入级联卡尔曼滤波-粒子滤波（KFPF），其中 KF 对 RSSI 进行平滑，PF 使用贝叶斯滤波执行非线性定位。
• 将 RSSI 和变化率作为 KF 状态，将路径损耗模型得到的距离估计作为输入给粒子滤波器，以实现二维/三维定位。
• 对环境特定的路径损耗曲线进行实验拟合，并将近距离/定位性能与滑动平均基线进行比较。

实验结果

研究问题

• RQ1 使用服务端过滤相较于基于 RSSI 的方法，iBeacons 的近距离精度可以提高多少？
• RQ2 在使用 iBeacons 时，贝叶斯滤波（卡尔曼滤波和粒子滤波）是否能改善室内定位的精度？
• RQ3 相较于仅使用 PF，级联 KF-PF 方法在二维和三维定位上是否能带来可测量的提升？
• RQ4 将计算卸载到服务器对基于 iBeacon 的 PBS/LBS 的能耗和延迟有何影响？

主要发现

• SRA 将近距离检测准确度提升了 29%，超过 Apple 的滑动平均方法。
• SKF 将近距离检测准确性提升了 32%，超过 Apple 的滑动平均方法。
• KFPF 级联在二维定位的精度提升为 28.16%，三维定位为 25.59%，相较于单独使用 PF。
• 在二维环境中定位误差低至 0.70 米，在三维环境中为 0.947 米。
• 实验使用真实世界式的房间和搭载 Gimbal Series 10 信标的 iPhone 6s Plus，环境特定的路径损耗模型拟合 RSSI 数据。