ACMI: FM-Based Indoor Localization via Autonomous Fingerprinting

被引:32
|
作者
Yoon, Sungro [1 ]
Lee, Kyunghan [2 ]
Yun, YeoCheon [2 ]
Rhee, Injong [3 ]
机构
[1] Microsoft, Bellevue, WA 98005 USA
[2] UNIST, Sch Elect & Comp Engn, Ulsan, South Korea
[3] N Carolina State Univ, Dept Comp Sci, Raleigh, NC 27695 USA
基金
美国国家科学基金会;
关键词
Indoor localization; FM signal; signal fingerprint; pattern matching; PATH-LOSS; PROPAGATION; PREDICTION; MODELS;
D O I
10.1109/TMC.2015.2465372
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We present ACMI, an FM-based indoor localization system that does not require proactive site profiling. ACMI constructs the fingerprint database based on pure estimation of indoor received signal strength (RSS) distribution, where only the signals transmitted from commercial FM radio stations are used. Based on extensive field measurement study, we established our own signal propagation model that harnesses FM radio characteristics and open information of FM transmission towers in combination with the floor-plan of a building. Output of the model is an RSS fingerprint database. Using the fingerprint database as a knowledge base, ACMI refines a positioning result via the two-step process; parameter calibration and path matching, during its runtime. Without site profiling, our evaluation indicates that ACMI in seven campus locations and three downtown buildings using eight distinguished FM stations finds positions with only about 6 and 10 meters of errors on average, respectively.
引用
收藏
页码:1318 / 1332
页数:15
相关论文
共 50 条
  • [1] Autonomous WiFi Fingerprinting for Indoor Localization
    Dai, Shilong
    He, Liang
    Zhang, Xuebo
    2020 ACM/IEEE 11TH INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS (ICCPS 2020), 2020, : 141 - 150
  • [2] FM-Based Positioning via Deep Learning
    Zheng, Shilian
    Hu, Jiacheng
    Zhang, Luxin
    Qiu, Kunfeng
    Chen, Jie
    Qi, Peihan
    Zhao, Zhijin
    Yang, Xiaoniu
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2024, 42 (09) : 2568 - 2584
  • [3] An Approach for Autonomous Recalibration of Fingerprinting-based Indoor Localization Systems
    Fet, Ngewi
    Handte, Marcus
    Marron, Pedro Jose
    12TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS - IE 2016, 2016, : 24 - 31
  • [4] KF-KNN: Low-Cost and High-Accurate FM-Based Indoor Localization Model via Fingerprint Technology
    Du, Canyang
    Peng, Bao
    Zhang, Zhaobo
    Xue, Weicheng
    Guan, Mingxiang
    IEEE ACCESS, 2020, 8 : 197523 - 197531
  • [5] Indoor Localization Using FM Radio Signals: A Fingerprinting Approach
    Moghtadaiee, Vahideh
    Dempster, Andrew G.
    Lim, Samsung
    2011 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION, 2011,
  • [6] A WLAN Fingerprinting Based Indoor Localization Technique via Artificial Neural Network
    Farid, Zahid
    Khan, Imran Ullah
    Scavino, Edgar
    Abd Rahman, Mohd Amiruddin
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2019, 19 (07): : 157 - 165
  • [7] RF Fingerprinting Based GSM Indoor Localization
    Buyruk, Hasan
    Keskin, A. Kenan
    Sendil, Seyma
    Celebi, Hasari
    Partal, Hakan P.
    Ileri, Omer
    Zeydan, Engin
    Ergut, Salih
    2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2013,
  • [8] Dictionary learning based fingerprinting for indoor localization
    Kumar, Chirag
    Rajawat, Ketan
    2018 TWENTY FOURTH NATIONAL CONFERENCE ON COMMUNICATIONS (NCC), 2018,
  • [9] A sensor based indoor localization through fingerprinting
    Haque, Israat Tanzeena
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2014, 44 : 220 - 229
  • [10] Autonomous Signal Source Displacement Detection and Recalibration of Fingerprinting-based Indoor Localization Systems
    Fet, Ngewi
    Handte, Marcus
    Marron, Pedro Jose
    2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2017,