SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization

被引:1
|
作者
Mottakin, Khairul [1 ]
Davuluri, Kiran [1 ]
Allison, Mark [2 ]
Song, Zheng [1 ]
机构
[1] Univ Michigan, Dept Comp & Informat Sci, Dearborn, MI 48128 USA
[2] Univ Michigan, Coll Innovat & Technol, Flint, MI 48502 USA
基金
美国国家科学基金会;
关键词
Channel State Information (CSI); dead reckoning; indoor localization; smartphone sensor fusion; walking direction estimation; WiFi sensing; RECOGNITION;
D O I
10.3390/s24196327
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.
引用
收藏
页数:23
相关论文
共 50 条
  • [21] Indoor Navigation with a Smartphone Fusing Inertial and WiFi Data via Factor Graph Optimization
    Nowicki, Michal
    Skrzypczynski, Piotr
    MOBILE COMPUTING, APPLICATIONS, AND SERVICES (MOBICASE 2015), 2015, 162 : 280 - 298
  • [22] APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information
    Shang, Jianga
    Gu, Fuqiang
    Hu, Xuke
    Kealy, Allison
    SENSORS, 2015, 15 (10) : 27251 - 27272
  • [23] A novel particle filter approach for indoor positioning by fusing WiFi and inertial sensors
    Zhu Nan
    Zhao Hongbo
    Feng Wenquan
    Wang Zulin
    CHINESE JOURNAL OF AERONAUTICS, 2015, 28 (06) : 1725 - 1734
  • [24] MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multisource Sensors Under Multiple Scene Conditions
    Liu, Jianhua
    Zeng, Baoshan
    Li, Songnian
    Zlatanova, Sisi
    Yang, Zhijie
    Bai, Mingchen
    Yu, Bing
    Wen, Danqi
    IEEE SENSORS JOURNAL, 2024, 24 (16) : 26320 - 26333
  • [25] A novel particle filter approach for indoor positioning by fusing WiFi and inertial sensors
    Zhu Nan
    Zhao Hongbo
    Feng Wenquan
    Wang Zulin
    Chinese Journal of Aeronautics, 2015, 28 (06) : 1725 - 1734
  • [26] A novel particle filter approach for indoor positioning by fusing WiFi and inertial sensors
    Zhu Nan
    Zhao Hongbo
    Feng Wenquan
    Wang Zulin
    Chinese Journal of Aeronautics, 2015, (06) : 1725 - 1734
  • [27] Accurate WiFi Localization by Fusing a Group of Fingerprints via a Global Fusion Profile
    Guo, Xiansheng
    Li, Lin
    Ansari, Nirwan
    Liao, Bin
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (08) : 7314 - 7325
  • [28] A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors
    Li, You
    Zhuang, Yuan
    Lan, Haiyu
    Zhou, Qifan
    Niu, Xiaoji
    El-Sheimy, Naser
    IEEE COMMUNICATIONS LETTERS, 2016, 20 (01) : 169 - 172
  • [29] Real-time and Accurate Acoustic Indoor Localization With a Smartphone
    Wen, Xiangji
    Huang, Danjie
    Fang, Wenhao
    Lin, Feng
    Huang, Yifan
    Wang, Zhi
    PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING 2015 (ICMMCCE 2015), 2015, 39 : 976 - 981
  • [30] ALIGNING TRAINING MODELS WITH SMARTPHONE PROPERTIES IN WIFI FINGERPRINTING BASED INDOOR LOCALIZATION
    Manh Kha Hoang
    Schmalenstroeer, Joerg
    Haeb-Umbach, Reinhold
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 1981 - 1985