Zhang Lei, Huang Danjie, Wang Xinheng, Schindelhauer Christian, Wang Zhi
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.
School of Engineering and Computing, University of the West of Scotland, Paisley PA1 2BE, UK.
Sensors (Basel). 2017 Mar 30;17(4):727. doi: 10.3390/s17040727.
As the demand for indoor localization is increasing to support our daily life in large and complex indoor environments, sound-based localization technologies have attracted researchers' attention because they have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, they have high positioning accuracy and low-cost infrastructure. However, the non-line-of-sight (NLOS) phenomenon poses a great challenge and has become the technology bottleneck for practical applications of acoustic smartphone indoor localization. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements. In this paper, we focus on identifying NLOS components by characterizing the acoustic channels. Firstly, by analyzing indoor acoustic propagations, the changes of acoustic channel from the line-of-sight (LOS) condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios. Then, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, which considers the mitigation of the Doppler Effect and reduction of the computational complexity. Nine novel features have been extracted, and a support vector machine (SVM) classifier with a radial-based function (RBF) kernel is used to realize NLOS identification. The experimental result with an overall 98.9% classification accuracy based on a data set with more than 10 thousand measurements shows that the proposed identification approach and features are effective in acoustic NLOS identification for acoustic indoor localization via a smartphone. In order to further evaluate the performance of the proposed SVM classifier, the performance of an SVM classifier is compared with that of traditional classifiers based on logistic regression (LR) and linear discriminant analysis (LDA). The results also show that a SVM with the RBF kernel function method outperforms others in acoustic NLOS identification.
随着在大型复杂室内环境中支持我们日常生活的室内定位需求不断增加，基于声音的定位技术因其与商用现货（COTS）智能手机完全兼容、具有高定位精度和低成本基础设施等优势而吸引了研究人员的关注。然而，非视距（NLOS）现象带来了巨大挑战，已成为声学智能手机室内定位实际应用的技术瓶颈。通过识别和丢弃NLOS测量值，仅纳入视距（LOS）测量值可提高定位性能。在本文中，我们专注于通过表征声学信道来识别NLOS分量。首先，通过分析室内声学传播，将声学信道从视距（LOS）条件到NLOS条件的变化表征为两种传播场景之间信道增益和信道延迟的差异。然后，提出了一种基于互相关方法估计相对信道增益和延迟的有效方法，该方法考虑了多普勒效应的缓解和计算复杂度的降低。提取了九个新特征，并使用具有径向基函数（RBF）核的支持向量机（SVM）分类器来实现NLOS识别。基于一个包含一万多个测量值的数据集，总体分类准确率为98.9%的实验结果表明，所提出的识别方法和特征在通过智能手机进行声学室内定位的声学NLOS识别中是有效的。为了进一步评估所提出的SVM分类器的性能，将SVM分类器的性能与基于逻辑回归（LR）和线性判别分析（LDA）的传统分类器的性能进行了比较。结果还表明，具有RBF核函数方法的SVM在声学NLOS识别方面优于其他方法。
