Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems (2403.04333v1)

Published 7 Mar 2024 in eess.SP

Abstract: Indoor human positioning has become increasingly important for applications such as health monitoring, breath monitoring, human identification, safety and rescue operations, and security surveillance. However, achieving robust indoor human positioning remains challenging due to various constraints. Numerous attempts have been made in the literature to develop efficient indoor positioning systems (IPSs), with a growing focus on ML based techniques. This paper aims to compare and analyze current ML-based wireless techniques and approaches for indoor positioning, providing a comprehensive review of enabling technologies for human detection, positioning, and activity recognition. The study explores different input measurement data, including RSSI, TDOA, etc., for various IPSs. Key positioning techniques such as RSSI-based fingerprinting, Angle-based, and Time-based approaches are examined in conjunction with various ML methods. The survey compares the positioning accuracy, scalability, and algorithm complexity, with the goal of determining the suitable technology in various services. Finally, the paper compares distinct datasets focused on indoor localization, which have been published using diverse technologies. Overall, the paper presents a comprehensive comparison of existing techniques and localization models.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (51)
  1. A. M. Ossain and W.-S. Soh, “A survey of calibration-free indoor positioning systems,”  Comput. Commun., vol. 66, pp. 1–13, Jul. 2015.
  2. S.He and S.-H. G. Chan, “Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons,”  IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 466–490, 1st Quart., 2016.
  3. P.Bahl,V.N.Padmanabhan, “RADAR:an in-building RF based user location and tracking system,” in Proc. IEEE INFOCOM, TelAviv, Israel, 2000, pp.775–784.
  4. P. Singh and S. Agrawal, “TDOA Based Node Localization in WSN Using Neural Networks,” in Proc. Int. Conf. Commun. Syst. and Netw. Technol., Gwalior, 2013, pp. 400-404.
  5. P. Roy and C. Chowdhury, “A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectives,” CCF Trans. Pervasive Comput. Interact., vol. 4, pp. 1-21, Jan. 2022.
  6. A. K. Paul and T. Sato, “Localization in wireless sensor networks: A survey on algorithms, measurement techniques, applications and challenges,” J. Sensor Actuator Netw., vol. 6, no. 4, pp. 1–24, 2017.
  7. I. Guvenc and C.-C. Chong, “A survey on TOA based wireless localization and NLoS mitigation techniques,” IEEE Commun. Surveys Tuts., vol. 11, no. 3, pp. 107-124, 2009.
  8. K. Al Nuaimi and H. Kamel, “A survey of indoor positioning systems and algorithms,” in Proc. IEEE IIT, 2011, pp. 185-190.
  9. P. Roy and C. Chowdhury, “A survey of machine learning techniques for indoor localization and navigation systems,” J. Intell. Robot. Syst., vol. 101, no. 3, pp. 1-34, Mar. 2021.
  10. M. Depsey, “Indoor Positioning Systems in Healthcare”  Radianse Inc., White Paper, 2003.
  11. C. Becker and F. Durr, “On location models for ubiquitous computing,”  Personal and Ubiquitous Comput., vol. 9, no. 1, pp. 20-31, 2005.
  12. J. Hightower and G. Borriello, “Location sensing techniques,”  Technical Report UW CSE 2001-07-30, Dept. of Comput. Sci. and Eng., Univ. of Washington, 2001.
  13. S. He and S. . -H. G. Chan, “Tilejunction: Mitigating Signal Noise for Fingerprint-Based Indoor Localization,”  IEEE Trans. on Mobile Comput., vol. 15, no. 6, pp. 1554-1568, 1 Jun. 2016.
  14. B. T. Fang, “Simple solutions for hyperbolic and related position fixes,”  IEEE Trans. Aerosp. Electron. Syst., vol. 26, no. 5, pp. 748–753, Sep. 1990.
  15. Y.-T. Chan and K. C. Ho, “A simple and efficient estimator for hyperbolic location,” IEEE Trans. Signal Process., vol. 42, no. 8, pp. 1905–1915, Aug. 1994.
  16. A. Sonny and A. Kumar, “Fingerprint Image-Based Multi-Building 3D Indoor Wi-Fi Localization Using Convolutional Neural Networks” in Proc. Nat. Conf. on Commun. (NCC), Mumbai, India, 2022, pp. 106-111.
  17. M. Kjaergaard and C. Munk, “Hyperbolic location fingerprinting: A calibration-free solution for handling differences in signal strength,” in Proc. 6th Annu. IEEE Int. Conf. Pervasive Comput. Commun., Mar. 2008, pp. 110-116.
  18. J. Jung and H. Myung, “Indoor user localization using particle filter and NLOS ranging model,”  ICCAS 2010, 2010, pp. 2476-2479.
  19. G. Wang and K. Yang, “A new approach to sensor node localization using RSS measurements in wireless sensor networks,”  IEEE Trans. Wireless Commun., vol. 10, no. 5, pp. 1389–1395, May 2011.
  20. S. Y. Seidel and T. S. Rappaport, “914 MHz path loss prediction models for indoor wireless communications in multifloored buildings,”  IEEE Trans. Antennas Propag., vol. 40, no. 2, pp. 207–217, Feb. 1992.
  21. T.-H. Do and M. Yoo, “TDOA-based indoor positioning using vis- ible light,”  Photon. Netw. Commun., vol. 27, no. 2, pp. 80–88, 2014.
  22. A. Poulose and D. S. Han, “UWB indoor localization using deep learning LSTM networks,”  Appl. Sci., vol. 10, no. 18, pp. 6290, Sep. 2020.
  23. R. Mautz, “Indoor positioning technologies”  Swiss Geodetic Commission Geodetic-Geophysical Reports of Switzerland Zurich Tech. Rep., 86, 2012.
  24. M. Brunato and R. Battiti, “Statistical learning theory for location fingerprinting in wireless LANs”  Comput. Netw., vol. 47, pp. 825–845, 2005.
  25. Abdullah Cavdar and Kadir Turk. “Artificial Neural Network Based Indoor Positioning in Visible Light Communication Systems” in Proc. Int. Conf. Artif. Intell. and Data Process., IDAP 2018 (2019).
  26. M. Youssef and A. K. Agrawala,“Handling samples correlation in the Horus system,”  IEEE INFOCOM 2004, Hong Kong, vol. 2, pp. 1023– 1031, Mar. 2004.
  27. X. Lin and L. Zhang, “Intelligent and practical deep learning aided positioning design for visible light communication receivers,”  IEEE Commun. Lett., pp. 577–580, Dec. 2019.
  28. J. Yun and J. Woo, “A Comparative Analysis of Deep Learning and Machine Learning on Detecting Movement Directions Using PIR Sensors,”  IEEE Internet of Things J., vol. 7, no. 4, pp. 2855-2868, Apr. 2020.
  29. C. Basu and A. Rowe, “Tracking motion and proxemics using thermal-sensor array,”  arXiv:1511.08166, 2015, [online] Available: http://arxiv.org/abs/1511.08166.
  30. M. Bouazizi and T. Ohtsuki, “An Infrared Array Sensor-Based Method for Localizing and Counting People for Health Care and Monitoring,”  Proc. Inte. Conf. of the IEEE Eng. in Medicine &\&& Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 4151-4155.
  31. V. Chidurala and X. Li, “Occupancy Estimation Using Thermal Imaging Sensors and Machine Learning Algorithms,”  IEEE Sensors J., vol. 21, no. 6, pp. 8627-8638, 15 Mar. 2021.
  32. R. Takeda and K. Komatani, “Unsupervised adaptation of deep neural networks for sound source localization using entropy minimization,” in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Process. (ICASSP), 2017, pp. 2217-2221.
  33. S. Chakrabarty and E. A. P. Habets, “Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained With Noise Signals,”  IEEE J. of Selected Topics in Signal Process., vol. 13, no. 1, pp. 8-21, Mar. 2019.
  34. P. Bahl and V. N. Padmanabhan, “Enhancements to the RADAR user location and tracking system,”  Microsoft Corp., Tech. Rep. MSR-TR- 2000–12, Feb. 2000.
  35. H. Cui and N. Dahnoun,“High precision human detection and tracking using millimeter-wave radars,”  IEEE Aerosp. Electron. Syst. Mag., vol. 36, no. 1, pp. 22–32, Jan. 2021.
  36. R. Zhang and S. Cao, “Real-time human motion behavior detection via CNN using mmWave radar,”  IEEE Sensors J., vol. 3, no. 2, pp. 1–4, Feb. 2019.
  37. N. Lee and D. Han, “Magnetic indoor positioning system using deep neural network,” in Proc. Int. Conf. Indoor Positioning and Indoor Navigation (IPIN), Sapporo, 2017, pp. 1-8.
  38. HJ Bae and L Choi,“Large-Scale Indoor Positioning using Geomagnetic Field with Deep Neural Networks,” in Proc. IEEE Int. Conf. on Commun. (ICC), Shanghai, China, 2019, pp. 1-6.
  39. Chandra G., “Position Estimation at Indoors using Wi-Fi and Magnetic Field Sensors,” in Proc. of the IPIN 2022 WiP, Beijing, China, 5–7 Sep. 2022.
  40. H. J. Bae and L. Choi, “Large-scale indoor positioning using geomagnetic field with deep neural networks,”  Proc. IEEE Int. Conf. Commun. (ICC), pp. 1-6, May. 2019.
  41. D. Niculescu and R. University, “Positioning in Ad Hoc Sensor Networks,”  IEEE Netw. Mag., vol. 18, no. 4, Jul./Aug. 2004.
  42. S. Cheng, “An intelligent location system based on active RFID,” in Proc. Int. Conf. on Mach. Learn. and Cybern., Guilin, 2011, pp. 391-395.
  43. D. Zhu and J. Yan, “A Deep Learning Based Bluetooth Indoor Localization Algorithm by RSSI and AOA Feature Fusion,” in Proc. Int. Conf. on Comput., Inf. and Telecommun. Syst. (CITS), Piraeus, Greece, 2022, pp. 1-6.
  44. K. Kotrotsios and T. Orphanoudakis, “Accurate Gridless Indoor Localization Based on Multiple Bluetooth Beacons and Machine Learning,”  Proc. 7th Int. Conf. on Automat., Robot. and Appl. (ICARA), Prague, Czech Republic, 2021, pp. 190-194.
  45. M. Nowicki and J. Wietrzykowski, “Low-effort place recognition with Wi-Fi fingerprints using deep learning,” in Proc. Int. Conf. Automat., Springer, Cham, 2017, pp. 575–584.
  46. J. J. Caffery and G. L. Stuber, “Overview of radiolocation in CDMA cellular system,”  IEEE Commun. Mag., vol. 36, no. 4, pp. 38–45, Apr. 1998.
  47. Hamada Rizk, “Device-Invariant Cellular-Based Indoor Localization System Using Deep Learning,”  ACM MobiSys 2019 on Rising Stars Forum, pp. 19–23, 2019.
  48. Hamada Rizk, “SoloCell: Efficient Indoor Localization Based on Limited Cell Network Information And Minimal Fingerprinting,” in Proc. ACM SIGSPATIAL Int. Conf. on Advances in Geographic Info. Syst. , 2019, pp. 604-605.
  49. Hamada Rizk and Moustafa Youssef, “Monodcell: A ubiquitous and low-overhead deep learning-based indoor localization with limited cellular information,” in Proc. of the 27th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Info. Syst., 2019, pp. 109–118.
  50. W. Vinicchayakul and S. Promwong, “Improvement of fingerprinting technique for UWB indoor localization,” in Proc. Joint Int. Conf. on Info. and Commun. Technol., Electron. and Elect. Eng. (JICTEE’14), 2014, pp. 1–5.
  51. Z. Tóth and J. Tamás “Miskolc IIS hybrid IPS: Dataset for hybrid indoor positioning” in Proc. 26th Int. Conf. Radioelektronika (RADIOELEKTRONIKA), Kosice, Slovakia, 2016, pp. 408-412.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets