A Survey on Detection, Tracking and Identification in Radio Frequency-Based Device-Free Localization

被引:37
作者
Denis, Stijn [1 ]
Berkvens, Rafael [1 ]
Weyn, Maarten [1 ]
机构
[1] Univ Antwerp, IMEC, Fac Appl Engn, IDLab, Sint Pietersvliet 7, B-2000 Antwerp, Belgium
关键词
localization; device-free; radio frequency; RF; wireless sensor networks; WSN; radio tomographic imaging; RTI; passive identification; passive tracking; passive detection; sensorless sensing; FREE PASSIVE LOCALIZATION; SIGNAL STRENGTH MODEL; RF SENSOR NETWORKS; RECOGNITION; TOMOGRAPHY; SYSTEM; TECHNOLOGY; ACCURACY; INTERNET; BODY;
D O I
10.3390/s19235329
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The requirement of active localization techniques to attach a hardware device to the targets that need to be located can be difficult or even impossible for certain applications. For this reason, there has been an increasing interest in tagless or device-free localization (DFL) approaches. In particular, the research domain of RF-based device-free localization has been steadily evolving since its inception slightly over a decade ago. Many novel techniques have been developed regarding the three core aspects of DFL: detection, tracking, and identification. The increasing use of channel state information (CSI) has contributed considerably to these developments. In particular, the progress it enabled regarding the exceptionally difficult 'identification problem' has been highly impressive. In this survey, we provide a comprehensive overview of this evolutionary process, describe essential DFL concepts and highlight several key techniques whose creation marked important milestones within this field of research. We do so in a structured manner in which each technique is categorized according to the DFL core aspect it emphasizes most. Additionally, we discuss current blocking issues within the state-of-the-art and suggest multiple high-level research directions which will aid in the search towards eventual solutions.
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页数:59
相关论文
共 146 条
[1]  
Abdelnasser H., 2015, P 16 ACM INT S MOB A, P277, DOI [DOI 10.1145/2746285.2755969, https://doi.org/10.1145/2746285.2755969]
[2]  
Abdelnasser H, 2015, IEEE CONF COMPUT, P17, DOI 10.1109/INFCOMW.2015.7179321
[3]  
Adler S, 2014, INT C INDOOR POSIT, P544, DOI 10.1109/IPIN.2014.7275527
[4]   Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas [J].
Aernouts, Michiel ;
Berkvens, Rafael ;
Van Vlaenderen, Koen ;
Weyn, Maarten .
DATA, 2018, 3 (02)
[5]  
Al-Husseiny A., 2012, P 2012 IEEE VEH TECH, P1
[6]   RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization [J].
Alippi, Cesare ;
Bocca, Maurizio ;
Boracchi, Giacomo ;
Patwari, Neal ;
Roveri, Manuel .
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2016, 15 (10) :2585-2598
[7]  
Aly Heba., 2013, Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, P541
[8]   Radio Tomography for Roadside Surveillance [J].
Anderson, Christopher R. ;
Martin, Richard K. ;
Walker, T. Owens ;
Thomas, Ryan W. .
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2014, 8 (01) :66-79
[9]  
[Anonymous], 2011, THESIS
[10]  
[Anonymous], 2015, PEER REPORT 20152