摘要翻译：
定位技术对于室内定位服务的发展具有重要意义。基于射频(RF)的指纹定位是最有前途的方法之一。然而，由于建立指纹数据库作为定位的先验步骤是费时费力的，因此将这种定位方法应用到实际环境中是一项具有挑战性的工作。另一个挑战是指纹中异常读数的存在降低了定位精度。为了解决这两个问题，我们提出了一种高效、稳健的室内定位方法。首先，我们将指纹数据库建模为一个三维张量，该张量表示指纹与访问点的位置和索引之间的关系。其次，我们提出了一种用于鲁棒指纹数据恢复的张量分解模型，该模型将部分观测张量分解为低秩张量和备用异常张量的叠加。第三，利用乘子交替方向法(ADMM)求解异常情形下的张量核范数完备凸优化问题。最后，我们在一个80m乘以20m的办公楼的地面真值数据集上验证了所提出的方法。实验结果表明，在相同的错误率为4%的情况下，该方法的采样率仅为10%，而现有方法的采样率为60%。此外，所提出的方法导致了更准确的定位（近20%,0.6百万）相比较的方法。
---
英文标题：
《A Tensor Completion Approach for Efficient and Robust Fingerprint-based
  Indoor Localization》
---
作者：
Yu Zhang, Xiao-Yang Liu
---
最新提交年份：
2017
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
--

---
英文摘要：
  The localization technology is important for the development of indoor location-based services (LBS). The radio frequency (RF) fingerprint-based localization is one of the most promising approaches. However, it is challenging to apply this localization to real-world environments since it is time-consuming and labor-intensive to construct a fingerprint database as a prior for localization. Another challenge is that the presence of anomaly readings in the fingerprints reduces the localization accuracy. To address these two challenges, we propose an efficient and robust indoor localization approach. First, we model the fingerprint database as a 3-D tensor, which represents the relationships between fingerprints, locations and indices of access points. Second, we introduce a tensor decomposition model for robust fingerprint data recovery, which decomposes a partial observation tensor as the superposition of a low-rank tensor and a spare anomaly tensor. Third, we exploit the alternating direction method of multipliers (ADMM) to solve the convex optimization problem of tensor-nuclear-norm completion for the anomaly case. Finally, we verify the proposed approach on a ground truth data set collected in an office building with size 80m times 20m. Experiment results show that to achieve a same error rate 4%, the sampling rate of our approach is only 10%, while it is 60% for the state-of-the-art approach. Moreover, the proposed approach leads to a more accurate localization (nearly 20%, 0.6m improvement) over the compared approach.
---
PDF链接：
https://arxiv.org/pdf/1712.01722