This paper provides a robust, scalable Bluetooth Low-Energy (BLE) based indoor localization solution using commodity hardware. While WiFi-based indoor localization has been widely studied, BLE has emerged a key technology for contact-tracing in the current pandemic. To accurately estimate distance using BLE on commercial devices, systems today rely on Receiver Signal Strength Indicator(RSSI) which suffers from sampling bias and multipath effects. We propose a new metric: Packet Reception Probability (PRP) that builds on a counter-intuitive idea that we can exploit packet loss to estimate distance. We localize using a Bayesian-PRP formulation that also incorporates an explicit model of the multipath. To make deployment easy, we do not require any hardware, firmware, or driver-level changes to off-the-shelf devices, and require minimal training. PRP can achieve meter level accuracy with just 6 devices with known locations and 12 training locations. We show that fusing PRP with RSSI is beneficial at short distances < 2m. Beyond 2m, fusion is worse than PRP, as RSSI becomes effectively de-correlated with distance. Robust location accuracy at all distances and ease of deployment with PRP can help enable wide range indoor localization solutions using BLE.

翻译：本文提出了一种基于商业硬件的鲁棒、可扩展的蓝牙低功耗（BLE）室内定位解决方案。尽管基于WiFi的室内定位已得到广泛研究，但在当前疫情中，BLE已成为接触者追踪的关键技术。为了在商业设备上利用BLE精确估计距离，现有系统依赖接收信号强度指示器（RSSI），但该方法存在采样偏差和多径效应问题。我们提出了一种新指标：数据包接收概率（PRP），其核心理念颠覆常规——利用数据包丢失来估计距离。我们采用贝叶斯-PRP模型进行定位，该模型还融入了多径效应的显式模型。为了便于部署，我们无需对商用设备进行任何硬件、固件或驱动程序层面的修改，且仅需极少的训练数据。仅需6个已知位置的设备和12个训练位置，PRP即可实现米级精度。研究表明，在短距离（<2米）内，将PRP与RSSI融合有助于提升精度；但在超过2米的距离上，融合效果反而劣于单独使用PRP，因为此时RSSI与距离的关联性显著降低。PRP在所有距离上均能保持稳健的定位精度，且部署简便，有助于推广基于BLE的广泛室内定位解决方案。