Zhuang Yuan, Yang Jun, Li You, Qi Longning, El-Sheimy Naser
National ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, China.
Department of Geomatics Engineering, The University of Calgary, 2500 University Drive, NW, Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2016 Apr 26;16(5):596. doi: 10.3390/s16050596.
Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target's location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.
蓝牙低功耗（BLE）信标的室内无线定位在BLE协议发布后受到了广泛关注。在本文中，我们提出了一种算法，该算法结合了信道分离多项式回归模型（PRM）、信道分离指纹识别（FP）、异常值检测和扩展卡尔曼滤波（EKF），用于基于智能手机的BLE信标室内定位。所提出的算法分别使用FP和PRM来估计目标的位置以及目标与BLE信标的距离。我们比较了针对三个广告信道使用单独PRM的距离估计性能（即单独策略）与使用通过组合所有信道信息生成的聚合PRM的距离估计性能（即聚合策略）。还比较了单独策略和聚合策略基于FP的位置估计结果的性能。结果发现，单独策略可以提供更高的精度；因此，最好为每个BLE广告信道分别采用PRM和FP。此外，为了提高算法的鲁棒性，设计了一种两级异常值检测机制。从PRM和FP获得的距离和位置估计值被传递到第一个异常值检测，以生成用于EKF的改进距离估计值。在EKF过程之后，进一步执行基于统计测试的第二个异常值检测算法以去除异常值。所提出的算法通过各种现场实验进行了评估。结果表明，在BLE信标密集部署（每9米1个信标）的情况下，所提出的算法在90%的时间内实现了<2.56米的精度，比传播模型（PM）+EKF算法的<3.99米性能提高了35.82%，比FP+EKF算法的<3.04米精度提高了15.77%。在稀疏部署（每18米1个信标）的情况下，所提出的算法在90%的时间内实现了<3.88米的精度，比PM+EKF算法的<8.00米精度提高了49.58%，比FP+EKF算法的<4.94米性能提高了21.41%。因此，所提出的算法对于提高信标部署稀疏环境中的定位精度特别有用。
