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Direct-path based fingerprint extraction algorithm for indoor localization

Published: 05 November 2018 Publication History

Abstract

At present, there has been a booming interest in utilizing Channel State Information (CSI) extracted from MIMO-OFDM PHY layer to achieve precise indoor localization. Compared with Received Signal Strength Indicator (RSSI), CSI as a fine-grained feature has a better performance on expressing the spatial and temporal features of wireless signal. As a result, CSI is more sensitive to the noise interference and multi-path. In this paper, we present a direct-path based fingerprint extraction algorithm for indoor localization in noisy and multi-path indoor environment. Our proposed algorithm firstly extracts the amplitude and phase measurements of direct-path from the raw CSI, and then calculates the unique fingerprint feature according to the filtered CSI. The experimental results show that our proposed algorithm improves the positioning accuracy up to 23.5% in complex indoor multipath environment.

References

[1]
Bozkurt, S., Elibol, G., Gunal, S., Yayan, U.: A comparative study on machine learning algorithms for indoor positioning. In: Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on. pp. 1--8. IEEE (2015)
[2]
Chowdhury, T.Z.: Using WiFi channel state information (CSI) for human activity recognition and fall detection. Ph.D. thesis, University of British Columbia (2018)
[3]
Cui, W., Wu, S., Wang, Y., Shan, Y.: A gossip-based tdoa distributed localization algorithm for wireless sensor networks. In: International Symposium on Instrumentation and Measurement, Sensor Network and Automation. pp. 841--846 (2014)
[4]
Eickhoff, R., Ellinger, F., Mosshammer, R., Weigel, R.: 3d-accuracy improvements for tdoa based wireless local positioning systems. In: GLOBECOM Workshops. pp. 1--6 (2008)
[5]
Fang, X., Nan, L., Jiang, Z., Chen, L.: Noise-aware fingerprint localization algorithm for wireless sensor network based on adaptive fingerprint kalman filter. Computer Networks 124, 97--107 (2017)
[6]
Fang, Y., Deng, Z., Xue, C., Jiao, J., Zeng, H., Zheng, R., Lu, S.: Application of an improved k nearest neighbor algorithm in wifi indoor positioning. In: China Satellite Navigation Conference (CSNC) 2015 Proceedings: Volume III. pp. 517--524. Springer (2015)
[7]
Gogolak, L., Pletl, S., Kukolj, D.: Neural network-based indoor localization in wsn environments. Acta Polytechnica Hungarica 10(6), 221--235 (2013)
[8]
Gong, W., Liu, J.: Sifi: Pushing the limit of time-based wifi localization using a single commodity access point. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2(1), 10 (2018)
[9]
Guowei, Z., Zhan, X., Dan, L.: Research and improvement on indoor localization based on rssi fingerprint database and k-nearest neighbor points. In: Communications, Circuits and Systems (ICCCAS), 2013 International Conference on. vol. 2, pp. 68--71. IEEE (2013)
[10]
Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Predictable 802.11 packet delivery from wireless channel measurements. In: ACM SIGCOMM 2010 Conference. pp. 159--170 (2010)
[11]
Hilsenrath, O., Wax, M.: Radio transmitter location finding for wireless communication network services and management (2000)
[12]
Hossain, A.M., Soh, W.S.: Cramer-rao bound analysis of localization using signal strength difference as location fingerprint. In: INFOCOM, 2010 Proceedings IEEE. pp. 1--9. IEEE (2010)
[13]
Jaffe, A., Wax, M.: Single-site localization via maximum discrimination multipath fingerprinting. IEEE Transactions on Signal Processing 62(7), 1718--1728 (2014)
[14]
Jiang, D.X., Ming-Qing, H.U., Chen, Y.Q., Liu, J.F., Zhou, J.Y.: Adaptive bluetooth location method based on kernel ridge regression. Application Research of Computers 27(9), 3487--3486 (2010)
[15]
Kupershtein, E., Wax, M., Cohen, I.: Single-site emitter localization via multipath fingerprinting. IEEE Transactions on signal processing 61(1), 10--21 (2013)
[16]
Liao, W., Fannjiang, A.: Music for single-snapshot spectral estimation: Stability and super-resolution. Applied & Computational Harmonic Analysis 40(1), 33--67 (2014)
[17]
Ma, H., Wang, K.: Fusion of rss and phase shift using the kalman filter for rfid tracking. IEEE Sensors Journal 17(11), 3551--3558 (2017)
[18]
Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., Snoussi, H.: Target tracking using machine learning and kalman filter in wireless sensor networks. IEEE Sensors Journal 14(10), 3715--3725 (2014)
[19]
Mahfouz, S., Mourad-Chehade, F., Honeine, P., Snoussi, H., Farah, J.: Kernel-based localization using fingerprinting in wireless sensor networks. In: Signal Processing Advances in Wireless Communications (SPAWC), 2013 IEEE 14th Workshop on. pp. 744--748. IEEE (2013)
[20]
Qian, K., Wu, C., Yang, Z., Liu, Y., Jamieson, K.: Widar: Decimeter-level passive tracking via velocity monitoring with commodity wifi. In: The ACM International Symposium. pp. 1--10 (2017)
[21]
Qian, K., Wu, C., Yang, Z., Zhou, Z., Wang, X., Liu, Y.: Enabling phased array signal processing for mobile wifi devices. IEEE Transactions on Mobile Computing PP(99), 1--1 (2017)
[22]
Schmidt, R.O.: Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas & Propagation 34(3), 276--280 (1986)
[23]
Shata, A.M., El-Hamid, S.S.A., Heiba, Y.A., Nasr, O.A.: Multi-site fusion for wlan based indoor localization via maximum discrimination fingerprinting. In: Innovative Trends in Computer Engineering (ITCE), 2018 International Conference on. pp. 214--218. IEEE (2018)
[24]
Tran, T.D., Oliveira, J., Silva, J.S., Pereira, V., Sousa, N., Raposo, D., Cardoso, F., Teixeira, C.: A scalable localization system for critical controlled wireless sensor networks. In: Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2014 6th International Congress on. pp. 302--309. IEEE (2014)
[25]
Wang, Y., Xu, X.: Indoor localization service based on the data fusion of wifi and rfid. In: IEEE International Conference on Web Services. pp. 180--187 (2016)
[26]
Wang, Y., Ho, K.C.: An asymptotically efficient estimator in closed-form for 3-d aoa localization using a sensor network. IEEE Transactions on Wireless Communications 14(12), 6524--6535 (2015)
[27]
Want, R., Hopper, A., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems 10(1), 91--102 (1992)
[28]
Wax, M., Meng, Y., Hilsenrath, O.: Subspace signature matching for location ambiguity resolution in wireless communication systems (2000)
[29]
Wu, C., Yang, Z., Zhou, Z., Qian, K., Liu, Y., Liu, M.: Phaseu: Real-time los identification with wifi. In: Computer Communications (INFOCOM), 2015 IEEE Conference on. pp. 2038--2046. IEEE (2015)
[30]
Xiong, J., Jamieson, K.: Arraytrack: a fine-grained indoor location system. Usenix (2013)
[31]
Xu, E., Ding, Z., Dasgupta, S.: Target tracking and mobile sensor navigation in wireless sensor networks. IEEE Transactions on Mobile Computing 12(1), 177--186 (2012)
[32]
Yang, Z., Zhou, Z., Liu, Y.: From rssi to csi: Indoor localization via channel response. ACM Computing Surveys 46(2), 1--32 (2013)
[33]
Zhu, D., Zhao, B., Wang, S.: Mobile target indoor tracking based on multi-direction weight position kalman filter. Computer Networks (2018)

Cited By

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  • (2024)Wi-Fi sensing based person identification and activity recognition using two-phase deep learning modelEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107904132(107904)Online publication date: Jun-2024
  • (2022)An improved Wi-Fi sensing-based human activity recognition using multi-stage deep learning modelSoft Computing10.1007/s00500-021-06534-226:9(4509-4518)Online publication date: 21-Mar-2022
  • (2019)Indoor localization based on subcarrier parameter estimation of LoS with wi-fiProceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3360774.3360785(80-89)Online publication date: 12-Nov-2019
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    cover image ACM Other conferences
    MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
    November 2018
    490 pages
    ISBN:9781450360937
    DOI:10.1145/3286978
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 05 November 2018

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    Author Tags

    1. Channel State Information
    2. Fingerprint Extraction
    3. Indoor Localization

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    MobiQuitous '18
    MobiQuitous '18: Computing, Networking and Services
    November 5 - 7, 2018
    NY, New York, USA

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    Overall Acceptance Rate 26 of 87 submissions, 30%

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    View all
    • (2024)Wi-Fi sensing based person identification and activity recognition using two-phase deep learning modelEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107904132(107904)Online publication date: Jun-2024
    • (2022)An improved Wi-Fi sensing-based human activity recognition using multi-stage deep learning modelSoft Computing10.1007/s00500-021-06534-226:9(4509-4518)Online publication date: 21-Mar-2022
    • (2019)Indoor localization based on subcarrier parameter estimation of LoS with wi-fiProceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3360774.3360785(80-89)Online publication date: 12-Nov-2019
    • (2019)A Survey on Human Behavior Recognition Using Channel State InformationIEEE Access10.1109/ACCESS.2019.29491237(155986-156024)Online publication date: 2019

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