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DFF-EDR:An Indoor Fingerprint Location Technology Using Dynamic Fusion Features of channel state information and Improved Edit Distance on Real Sequence

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China Communications 2021年第4期18卷 40-63页

作者： Ke Han Yunfei Xu Zhongliang Deng Jiawei Fu School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.

关键词： channel state information indoor positioning edit distance on real sequence dynamic parameters feature resolution

A Survey of Wi-Fi Sensing Techniques with channel state information

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ZTE Communications 2020年第3期18卷 57-63页

作者： MICHEN Liangqin TIAN Liping XU Zhimeng CHEN Zhizhang College of Physics and information Engineering Fuzhou UniversityFuzhou 350108China Department of Electrical and Computer Engineering Dalhousie UniversityHalifax B3H 4R2Canada

A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing systems operate are reviewed.It provides a brief introduction to the algorithms that perform signal processing,feature extraction and recognitions,including location,activity recognition,physiological signal detection and personal identification.Challenges and future trends of Wi-Fi sensing are also discussed in the end.

关键词： Wi-Fi sensing channel state information signal processing classifications feature extraction positioning location recognitions

Lossless Selection Combining Without channel state information

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Lossless Selection Combining Without Channel State Informati...

An Improved WiFi Indoor Localization Method Combining channel state information and Received Signal Strength

2008 IEEE International Conference on Communications(ICC 2008)

作者： Young Gil Kim Norman C.Beaulieu Dept.of Electrical and Computer Engineering University of Seoul SeoulKorea 130-743 Dept.of Electrical and Computer Engineering University of Alberta Edmonton AlbertaCanada T6G 2G7

A signal-plus-noise （S+N ） energy selection combining （S+N-ESC） scheme for binary phase shift keying （BPSK） signaling is proposed. The S+N-ESC selects the branch with the maximum S+N energy for detection. The advantage of the S+N-ESC is that it requires no channel estimation for diversity branch selection. The bit error rate （BER） performance of the S+N-ESC is analyzed for BPSK signaling in independent and identically distributed （i.i.d.） Rayleigh fading channels. We show that the performance of the S+N-ESC is the same as conventional signal-to-noise ratio selection combining （SNR-SC）,which requires estimation of the fading amplitudes for branch selection.

关键词： Diversity reception channel state information Binary phase shift keying Bit error rate channel estimation Signal analysis Performance analysis Fading Signal to noise ratio Amplitude estimation

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An Improved WiFi Indoor Localization Method Combining Channe...

Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model

第36届中国控制会议

作者： Linsheng Zhao Hongpeng Wang Pengpeng Li Jingtai Liu Institute of Robotics and Automatic information System Nankai University Tianjin Key Laboratory of Intelligent Robotics

ISBN: (纸本)9781538629185

WiFi indoor localization has attracted much attention owing to the pervasive penetration of wireless local area networks（WLANs） and WiFi enabled mobile devices.Traditional WiFi indoor localization systems rely on received signal strength（RSS）,which is instability and low space distinguish ability.Recently,channel state information（CSI） has been adopted instead of RSS and proven to be an efficient method.However,CSI raw data is sensitive to environment change.In this paper,we propose an improved WiFi indoor localization system combining CSI and RSS.Firstly,this study applies a time domain filtering method to reduce CSI data dynamic range.Then,we use the coherence bandwidth for CSI dimensional reduction.Afterwards,we extract a robust positioning feature from CSI and RSS.Finally,an improved weighted k-nearest neighbor algorithm based on kernel methods is used to estimate the location.The experiments demonstrate the effectiveness of the proposed system.

关键词： WiFi fingerprint indoor localization channel state information kernel methods

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1827-1851页

作者： Jian Zhao Shangwu Chong Liang Huang Xin Li Chen He Jian Jia School of information Science and Technology Northwest UniversityXi’an710127China School of Mathematics Northwest UniversityXi’an710127China

In this paper,we propose an improved deep residual network model to recognize human actions.Action data is composed of channel state information signals,which are continuous fine-grained signals.We replaced the traditional identity connection with the shrinking thresholdmodule.Themodule automatically adjusts the threshold of the action data signal,and filters out signals that are not related to the principal components.We use the attention mechanism to improve the memory of the network model to the action signal,so as to better recognize the action.To verify the validity of the experiment more accurately,we collected action data in two different environments.The experimental results show that the improved network model is much better than the traditional network in recognition.The accuracy of recognition in complex places can reach 92.85%,among which the recognition rate of raising hands is up to 96%.We combine the improved residual deep network model with channel state information action data,and prove the effectiveness of our model for classification through experimental data.

关键词： Action recognition residual deep network network model channel state information

CSI-based passive intrusion detection bound estimation in indoor NLoS scenario

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Fundamental Research 2023年第6期3卷 988-996页

作者： Linqing Gui Wenyang Yuan Fu Xiao Nanjing University of Posts and Telecommunications College of ComputerJiangsu 210023China

Passive intrusion detection is important for many indoor safety applications.By utilizing widespread WiFi infrastructures,channel state information(CSI)based intrusion detection methods have attractive advantages such as low cost,non-line-of-sight(NLoS)support,and privacy protection.However,existing CSI-based methods lack in-depth and intensive analysis of intrusion detection bound.To the best of our knowledge,this is the first work that precisely characterizes and models CSI-based intrusion detection bound in typical indoor NLoS scenario.A bound is defined as an intruder’s farthest position from the transmitter during intrusion detection.To derive a model for the bound,we first derived an intrusion-disturbed NLoS channel model by analyzing the influence of human intrusion on the NLoS wireless channel.Subsequently,based on the channel model,we further derived an intrusion detection bound model.Based on the derived bound model,we proposed a practical system to estimate the real intrusion detection bound.Extensive experiments were conducted based on practical systems.The experimental and simulation results verified and demonstrated the effectiveness of the derived bound model.Our work not only reveals the fundamental performance limit of the basic intrusion detection method in an indoor NLoS scenario,but also provides a valuable reference for bound estimation for other fine-grained wireless sensing applications.

关键词： Intrusion detection Wireless sensing Commodity WiFi channel state information Non line-of-sight

Device-free human micro-activity recognition method using WiFi signals

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Geo-Spatial information Science 2019年第2期22卷 128-137,I0005页

作者： Mohammed A.A.Al-qaness School of Computer Science Wuhan UniversityWuhanChina

Human activity tracking plays a vital role in human–computer interaction.Traditional human activity recognition(HAR)methods adopt special devices,such as cameras and sensors,to track both macro-and micro-activities.Recently,wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment.This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information(CSI)of wireless signals.Different from existed CSI-based microactivity recognition methods,the proposed method extracts both amplitude and phase information from CSI,thereby providing more information and increasing detection accuracy.The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity.We applied a machine learning algorithm to recognize the proposed micro-activities.The proposed method has been evaluated in both line of sight(LOS)and none line of sight(NLOS)scenarios,and the empirical results demonstrate the effectiveness of the proposed method with several users.

关键词： Human activity recognition channel state information WiFi device-free microactivity recognition machine learning

Literature Review on Wireless Sensing Wi-Fi Signal-Based Recognition of Human Activities

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Tsinghua Science and Technology 2018年第2期23卷 203-222页

作者： Chao Wang Siwen Chen Yanwei Yang Feng Hu Fugang Liu Jie Wu Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai 200444 China. Science and Technology Heilongjiang University Harbin 150022 China. Department of Computer and informationSciences Temple University PA 19122-6096 USA.

With the rapid development and wide deployment of wireless technology, Wi-Fi signals have no longer been confined to the Internet as a communication medium. Wi-Fi signals will be modulated again by human actions when propagating indoors, carrying rich human body state information. Therefore, a novel wireless sensing technology is gradually emerging that can realize gesture recognition, human daily activity detection, identification,indoor localization and human body tracking, vital signs detection, imaging, and emotional recognition by extracting effective feature information about human actions from Wi-Fi signals. Researchers mainly use channel state information or frequency modulated carrier wave in their current implementation schemes of wireless sensing technology, called "Walls have eyes", and these schemes cover radio-frequency technology, signal processing technology, and machine learning. These available wireless sensing systems can be used in many applications such as smart home, medical health care, search-and-rescue, security, and with the high precision and passively device-free through-wall detection function. This paper elaborates the research actuality and summarizes each system structure and the basic principles of various wireless sensing applications in detail. Meanwhile, two popular implementation schemes are analyzed. In addition, the future diversely application prospects of wireless sensing systems are presented.

关键词： channel state information frequency modulated carrier wave human activities recognition wireless sensing applications Wi-Fi

Communicating Is Crowdsourcing: Wi-Fi Indoor Localization with CSI-Based Speed Estimation

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Journal of Computer Science & Technology 2014年第4期29卷 589-604页

作者：蒋志平惠维李向阳唐少杰赵季中韩劲松赵鲲王志肖波 School of Electronic and information Engineering Xi’an Jiaotong University CCF ACM IEEE Department of Computer Science Illinois Institute of Technology Chicago IL 60616 U.S.A. Department of information Science University of Texas at Dallas Richardson TX U.S.A. Network Center The Aviation University of Air Fone

Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location- based service （LBS）. Among them, the most popular solutions are the Wi-Fi fingerprint-based approaches. The core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner. However the low participation rate blocks the practical use. In this work, we propose a passive crowdsourcing channel state information （CSI） based indoor localization scheme, C2IL. Despite a crowdsourcing based approach, our scheme is totally transparent to the client and the only requirement is to connect to our 802.11n access points （APs）. C2IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI. Knowing the walking speed of a client and its surrounding APs, a graph matching algorithm is employed to extract the received signal strength （RSS） fingerprints and establish the fingerprint map. For localization phase, we design a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking. We develop and deploy a practical working system of C2IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2 m at 80% time in a very complex indoor environment.

关键词： channel state information speed estimation fingerprint map graph matching indoor localization