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Radioengineering

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Proceedings of Czech and Slovak Technical Universities

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June 2026, Volume 35, Number 2 [DOI: 10.13164/re.2026-2]

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R. Zhou, P. Lai, L. Chen, Z. Hu, L. Qian [references] [full-text] [DOI: 10.13164/re.2026.0195] [Download Citations]
Mid-Air Hand Rehabilitation Evaluation and Virtual Training System

This paper presents the design of a mid-air hand rehabilitation evaluation and virtual training system based on Leap Motion and gesture recognition algorithms. The system aims to provide a scientifically-grounded and accessible home-based rehabilitation solution for patients with hand injuries. It employs a spatio-temporal attention-enhanced multi-scale residual graph convolutional network algorithm for gesture recognition. Following this, specific joint angles are calculated and 14 representative gesture scores are derived. Weights are assigned to each scoring item via ridge regression to achieve a quantitative assessment. The rehabilitation training module comprises two modes: interactive turn-based games and music rhythm games, designed to train hand movement and dexterity. The system was subsequently tested to evaluate the performance of both the assessment function and the two training games. Results show an average gesture recognition accuracy of 94.86%. Furthermore, the reliability scores for both training games exceeded 90%. These findings demonstrate that the system achieves good accuracy in gesture recognition and effective assessment of hand rehabilitation progress.

  1. MOUNTCASTLE, V. B. The Sensory Hand: Neural Mechanisms of Somatic Sensation. 1st ed., Cambridge (Mass, USA): Harvard University Press, 2005. ISBN: 9780674019744
  2. MAYRHOFER-SCHMID, M., AMAN, M., STOLLE, A., et al. Analysis of epidemiology, etiology and injury patterns in 2,179 digit amputations. Scientific Reports, 2025, vol. 15, no. 1, p. 1–8. DOI: 10.1038/s41598-025-18983-y
  3. TAMULEVICIUS, M., BUCHER, F., DASTAGIR, N., et al. Demographic shifts reshaping the landscape of hand trauma: A comprehensive single-center analysis of changing trends in hand injuries from 2007 to 2022. Injury Epidemiology, 2024, vol. 11, no. 1, p. 1–10. DOI: 10.1186/s40621-024-00510-8
  4. SAVAS, S., ALTUNTAS, S. H., USLUSOY, F., et al. Epidemiology and injury characteristics of children with acute traumatic hand injuries undergoing surgery: A 10-year retrospective cohort study. Ulusal Travma Ve Acil Cerrahi Dergis Turkish Journal of Trauma & Emergency Surgery, 2025, vol. 31,
  5. no. 8, p. 729–738. DOI: 10.14744/tjtes.2025.07892
  6. MOELLHOFF, N., THRONER, V., FRANK, K., et al. Epidemiology of hand injuries that presented to a tertiary care facility in Germany: A study including 435 patients. Archives of Orthopaedic and Trauma Surgery, 2023, vol. 143, no. 3, p. 1715 to 1724. DOI: 10.1007/s00402-022-04617-9
  7. KANKAM, H. K., IBRAHIM, H., LIEW, M. S., et al. Epidemiology of adult hand injuries presenting to a tertiary hand surgery unit: A review of 4216 cases. Journal of Hand Surgery European Volume, 2024, vol. 49, no. 1, p. 48–53. DOI: 10.1177/17531934231195499
  8. LIM, J. X., LE, L. A. T., YEH, J. Z. Y., et al. The epidemiology and distribution of hand fractures in Singapore. Singapore Medical Journal,
  9. 2025, vol. 66, no. 9, p. 476–480. DOI: 10.4103/singaporemedj.SMJ-2021-334
  10. AKSAN, A., DURUSOY, R., ADA, S., et al. Epidemiology of injuries treated at a hand and microsurgery hospital. Acta Orthopaedica et Traumatologica Turcica, 2010, vol. 44, no. 5, p. 352–360. DOI: 10.3944/AOTT.2010.2372
  11. SLADE, J. F., MILEWSKI, M. D. Management of carpal instability in athletes. Hand Clinics, 2009, vol. 25, no. 3, p. 395 to 408. DOI: 10.1016/j.hcl.2009.05.002
  12. CHANG, J. H., SHIEH, S. J., KUO, L. C., et al. The initial anatomical severity in patients with hand injuries predicts future health-related quality of life. Journal of Trauma and Acute Care Surgery, 2011, vol. 71, no. 5, p. 1352–1358. DOI: 10.1097/TA.0b013e318216a56e
  13. EISELE, A., DERESKEWITZ, C., KUS, S., et al. Factors affecting time off work in patients with traumatic hand injuries - A biopsycho-social perspective. Injury, 2018, vol. 49, no. 10, p. 1822 to 1829. DOI: 10.1016/j.injury.2018.07.012
  14. KOLOVICH, G. P., HEIFNER, J. J. Proximal interphalangeal joint dislocations and fracture-dislocations. Journal of Hand Surgery European Volume, 2023, vol. 48, no. 2_SUPPL, p. 27S–34S. DOI: 10.1177/17531934231183259
  15. CANTERO-TELLEZ, R. A global proprioception concept after hand injury - Patient report. Journal of Hand Therapy, 2024, vol. 37, no. 2, p. 293–295. DOI: 10.1016/j.jht.2023.10.002
  16. AHMAD, K. A., HIGASHI, T., YOSHIDA, K. Dynamic hand gesture recognition by hand landmark classification using long short-term memory. Pertanika Journal of Science and Technology, 2025, vol. 33, no. S2, p. 73–84. DOI: 10.47836/pjst.33.S2.05
  17. TRAN, D. S., HO, N. H., YANG, H. J., et al. Real-time hand gesture spotting and recognition using RGB-D camera and 3D convolutional neural network. Applied Sciences-Basel, 2020, vol. 10, no. 2, p. 1–15. DOI: 10.3390/app10020722
  18. LIU, Q., CAI, M., LIU, D., et al. ESS MS-G3D: Extension and supplement shift MS-G3D network for the assessment of severe mental retardation. Complex & Intelligent Systems, 2024, vol. 10, no. 2, p. 2401–2419. DOI: 10.1007/s40747-023-01275-1
  19. CHEN, Z., LAN, X., ZENG, Y., et al. Multiscale spatiotemporal residual graph convolutional neural network for action recognition. (in Chinese) Journal of Naval Aviation University, 2025, vol. 40, no. 2, p. 292–302. DOI: 10.7682/j.issn.2097-1427.2025.02.007
  20. YU, H., NELSON, A., ERDEN, M. S. A multi-layered quantitative assessment approach for hand spasticity based on a cable-actuated hand exoskeleton. IEEE Robotics and Automation Letters, 2025, vol. 10, no. 3, p. 2335–2342. DOI: 10.1109/LRA.2025.3530320
  21. FANG, Q., MAHMOUD, S. S., GU, X., et al. A novel multi standard compliant hand function assessment method using an infrared imaging device. IEEE Journal of Biomedical and Health Informatics, 2019, vol. 23, no. 2, p. 758–765. DOI: 10.1109/JBHI.2018.2837380
  22. LI, C. G., CHENG, L., YANG, H., et al. An automatic rehabilitation assessment system for hand function based on leap motion and ensemble learning. Cybernetics and Systems, 2020, vol. 52, no. 1, p. 3–25. DOI: 10.1080/01969722.2020.1827798
  23. NATH, D., SINGH, N., BANDUNI, O., et al. Variable handle resistance based joystick for post-stroke neurorehabilitation training of hand and wrist in upper extremities. IEEE Transactions on Human-Machine Systems, 2024, vol. 55, no. 1, p. 93–101. DOI: 10.1109/THMS.2024.3486123
  24. GHERMAN, B., ZIMA, I., VAIDA, C., et al. Robotic systems for hand rehabilitation- past, present and future. Technologies, 2025, vol. 13, no. 1, p. 1–51. DOI: 10.3390/technologies13010037
  25. JHA, C. K., SHUKLA, Y., MUKHERJEE, R., et al. A glove-based virtual hand rehabilitation system for patients with post-traumatic hand injuries. IEEE Transactions on Biomedical Engineering, 2024, vol. 71, no. 7, p. 2033–2041. DOI: 10.1109/TBME.2024.3360888
  26. BAIN, G. I., POLITES, N., HIGGS, B. G., et al. The functional range of motion of the finger joints. Journal of Hand Surgery European Volume, 2015, vol. 40, no. 4, p. 406–411. DOI: 10.1177/1753193414533754
  27. CIENKI, A. The Cambridge Handbook of Gesture Studies. Cambridge (UK): Cambridge University Press, 2024. ISBN: 9781108486316
  28. BOBOS, P., MACDERMID, J. C., BOUTSIKARI, E. C., et al. Evaluation of the content validity index of the Australian/Canadian osteoarthritis hand index, the patient-rated wrist/hand evaluation and the thumb disability exam in people with hand arthritis. Health and Quality of Life Outcomes, 2020, vol. 18, no. 1, p. 1–9. DOI: 10.1186/s12955-020-01556-0
  29. PENG, L., ZHANG, C., ZHOU, L., et al. Traditional manual acupuncture combined with rehabilitation therapy for shoulder hand syndrome after stroke within the Chinese healthcare system: A systematic review and meta-analysis. Clinical Rehabilitation, 2018, vol. 32, no. 4, p. 429–439.
  30. DOI: 10.1177/0269215517729528
  31. SERBAN, A., GROSU-BULARDA, A., BORDEANU DIACONESCU, E. M., et al. Early rehabilitation versus conventional approaches in post-traumatic hand injuries with multiple lesions: Clinical outcomes and future directions. Medicina, 2025, vol. 61, no. 11, p. 1–24. DOI: 10.3390/medicina61112063

Keywords: Gesture recognition, hand rehabilitation, multi-scale residual graph convolution, attention mechanism, human-computer interaction

Y. D. Wang, X. Fan, M. M. Lu [references] [full-text] [DOI: 10.13164/re.2026.0208] [Download Citations]
Study on the Influence of Channel’s Non-Ideal Characteristic on Pseudo-Code Measurement Bias for GNSS Receiver

GNSS has been widely used in civilian and military areas due to its high precision. However, the channel’s non-ideal characteristic may introduce pseudo-code measurement bias to degrade positioning precision. There are few theoretical analyses for interpreting why the channel’s non-ideal characteristic may introduce pseudo-code measurement bias and what kind of channels will not introduce pseudo-code measurement bias. We have conducted a systematical study on the above issues. Firstly, we established an analysis model for interpreting the channel’ non-ideal characteristic’s influence on pseudo-code measurement bias. Based on the analysis model, we proposed four sufficient conditions for unbiased pseudo-code measurement. Secondly, we applied the analysis model to typical channels, i.e. sine type group delay channel, frequency-domain anti-jamming channel and space-domain anti-jamming channel, to evaluate the channel’s influence on pseudo-code measurement bias. Finally, we conducted simulations to evaluate the theoretical analysis’ correctness. Results show that these typical channels do not introduce pseudo-code measurement bias, which are consistent with theoretical analysis. As a result, the correctness of the proposed four sufficient conditions is verified so that the proposed four sufficient conditions can be used to guide the design of unbiased pseudo-code measurement channel.

  1. KAPLAN, E. D., HEGARTY, C. J. Understanding GPS/GNSS: Principles and Applications. 3rd ed. Artech House, 2017. ISBN: 1630810580
  2. WANG, Y. D., YE, X. Z., CHEN, S., et al. Study on the influence of space-time adaptive processor on single point position and real time kinematic for GNSS antenna array anti-jamming receiver. IET Radar, Sonar & Navigation, 2025, vol. 19, no. 1, p. 1–11. DOI: 10.1049/rsn2.70035
  3. TAN, X. R., XU, J. N., LI, F. N., et al. A new GM(1,1) model suitable for short-term prediction of satellite clock bias. IET Radar, Sonar & Navigation, 2022, vol. 16, no. 12, p. 2040–2052. DOI: 10.1049/rsn2.12315
  4. YU, C., LI, Z. L., PENNA, N. T. Interferometric synthetic aperture radar atmospheric correction using a GPS_based iterative tropospheric
  5. decomposition model. Remote Sensing of Environment, 2018, vol. 204, p. 109–121. DOI: 10.1016/j.rse.2017.10.038
  6. KLOBUCHAR, J. A. Ionospheric time-delay algorithm for single frequency GPS users. IEEE Transactions on Aerospace and
  7. Electronic Systems, 1987, vol. AES-23, no. 3, p. 325–331. DOI: 10.1109/TAES.1987.310829
  8. VAN DIERENDONCK, A. J., FENTON, P., FORD, T. Theory and performance of narrow correlator spacing in a GPS receiver. Navigation, 1993, vol. 39, no. 3, p. 265–283. DOI: 10.1002/j.2161-4296.1992.tb02276.x
  9. BHUIYAN, M. Z. H., ZHANG, J., LOHAN, E. S., et al. Analysis of multipath mitigation techniques with land mobile satellite channel model. Radioengineering, 2012, vol. 21, no. 4, p. 1067-1077. ISSN: 1210-2512
  10. CHEN, F. Q., NIE, J. W., LI, B. Y., et al. Distortionless space-time adaptive processor for global navigation satellite system receiver. Electronic Letters, 2015, vol. 51, no. 25, p. 2138–2139. DOI: 10.1049/el.2015.2832
  11. DAI, X. Z., NIE, J. W., CHEN, F. Q., et al. Distortionless space time adaptive processor based on MVDR beamformer for GNSS receiver. IET Radar, Sonar & Navigation, 2017, vol. 11, no. 10, p. 1488–1494. DOI: 10.1049/iet-rsn.2017.0168
  12. WANG, Y. D., LIU, W. X., HUANG, L., et al. Distortionless pseudo-code tracking space-time adaptive processor based on the PI criterion for GNSS receiver. IET Radar, Sonar & Navigation, 2020, vol. 14, no. 12, p. 1984–1990. DOI: 10.1049/ietrsn.2020.0189
  13. ZHU, X. W., LI, Y. L., YONG, S. W. A novel definition and measurement method of group delay and its application. IEEE Transactions on Instrumentation and Measurement, 2009, vol. 58, no. 1, p. 229–233. DOI: 10.1109/TIM.2008.927197
  14. XIAO, Z. B., HUANG, Y. B., TANG, X. M., et al. Fourier decomposition model of group delay and its estimation method (in Chinese). Journal of National University of Defense Technology, 2021, vol. 43, no. 5, p. 72–79. DOI: 10.11887/j.cn.202105008
  15. LIU, Y. Q., CHEN, L., YANG, Y., et al. Theoretical evaluation of group delay on pseudorange bias. GPS Solutions, 2019, vol. 23, no. 3, p. 1–12. DOI: 10.1007/s10291-019-0861-z
  16. ZHOU, H. W., WEI, J. L., ZHANG, X. Q., et al. Research on nonideal property of payload core device on navigation satellite (in Chinese). Journal of Huazhong University of Science & Technology (Natural Science Edition), 2014, vol. 42, no. 7, p. 118–123.
  17. BETZ, J. W. Effect of linear time-invariant distortions on RNSS code tracking accuracy. In Proceedings of the 15th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 2002). Portland (Oregon, USA), 2002, p. 1636–1647.
  18. SOELLNER, M., KOHL, R., LUETKE, W., et al. The impact of linear and non-linear signal distortions on Galileo code tracking accuracy. In Proceedings of the 15th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GPS 2002), Portland (Oregon, USA), 2002, p. 1270–1285.
  19. HAUSCHILD, A., MONTENBRUCK, O. A study on the dependency of GNSS pseudorange biases on correlator spacing. GPS Solutions, 2016, vol. 20, no. 2, p. 159–171. DOI: 10.1007/s10291-014-0426-0
  20. HAUSCHILD, A., MONTENBRUCK, O. The effect of correlator and front-end design on GNSS pseudorange biases for geodetic receivers. Navigation, 2016, vol. 63, no. 4, p. 443–453. DOI: 10.1002/navi.165
  21. MULLER, T. Performance degradation in GPS-receivers caused by group delay variations of SAW-filters. In 1998 IEEE MTT-S International Microwave Symposium Digest. Baltimore (MD, USA), 1998, vol. 2, p. 495–498.
  22. ZHANG, T. Q., ZHANG, X. M., LU, M. Q. Effect of frequency domain anti-jamming filter on satellite navigation signal tracking performance. In China Satellite Navigation Conference 2013. Wuhan (China), 2013, p. 507–516. DOI: 10.1007/978-3-64237398-5_46
  23. VAN VEEN, B. D., BUCKLE, K. M. Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine, 1988, vol. 5, no. 2, p. 4–24. DOI: 10.1109/53.665
  24. FU, Z., HOMBOSTEL, A., HAMMESFAHR, J., et al. Suppression of multipath and jamming signals by digital beamforming for GPS/Galileo applications. GPS Solutions, 2003, vol. 6, no. 4, p. 257–264. DOI: 10.1007/s10291-002-0042-2
  25. COMPTON, R. T. The power-inversion adaptive array: Concept and performance. IEEE Transactions on Aerospace and Electronic Systems, 1979, vol. AES-15, no. 6, p. 803–814. DOI: 10.1109/TAES.1979.308765

Keywords: Channel characteristic, pseudo-code measurement bias, GNSS receiver, non-ideal

L. Kirasamuthranon, J. Koseeyaporn, P. Wardkein [references] [full-text] [DOI: 10.13164/re.2026.0218] [Download Citations]
DSSZ-SM: A Simplified Chirp Coding Scheme with Inherent Clock Synchronization

This study proposes a novel double-slope chirp symbol, termed double–slope start zero stop minimum (DSSZ–SM), for efficient data communication. Unlike conventional chirp coding, which often involves complex generation and synchronization, the DSSZ–SM provides a simpler structure with inherent clock synchronization using a PWM-based generator. System performance is evaluated through analysis and simulations over additive white Gaussian noise (AWGN) and Rayleigh fading channels. Two asynchronous decoding methods, with and without an integrator, are compared. Results show that the non-integrator approach achieves lower error rates under both channel conditions. The proposed DSSZ–SM offers a simplified and robust alternative for efficient data communication.

  1. FOROUZAN, B. A. Analog Transmission, Data Communications and Networking. 4th ed. New York, USA: McGraw-Hill, 2007, p. 146–148. ISBN: 978-0-07-296775-3
  2. SKOLNIK, M. I. Introduction to Radar Systems. 2nd ed. Singapore: McGraw-Hill, 1981. Ch. 11 Extraction of Information and Waveform Design, p. 399–440. ISBN 0-07-066572-9
  3. LANCASTER, D. A “Chirp” - new radar technique. Electronics World, 1965, p. 42–59.
  4. GONZALEZ, J. E., PARDO, J. M., ASENSIO, A., et al. Digital signal generation for LPM-LPI radars. Electronics Letter, 2003, vol. 39, no. 5, p. 464–465. DOI: 10.1049/el:20030316
  5. SEMTECH. A Brief History of LoRa®: Three Inventors Share Their Personal Story at The Things Conference. [Online] Cited 2025-02
  6. 22. Available at: https://blog.semtech.com/a-brief-history-of-lora three-inventors-share-their-personal-story-at-the-things-conference
  7. LIU, S., YU, P., ZHOU, F., et al. Research on chirp signal denoising algorithm based on IoT. In 2020 International Wireless Communications and Mobile Computing (IWCMC). Limassol (Cyprus), 2020, p. 331–336. DOI: 10.1109/IWCMC48107.2020.9148076
  8. ROY, A., NEMADE, H. B., BHATTACHARJEE, R. Symmetry chirp modulation waveform design for LEO satellite IoT communication. IEEE Communications Letters, 2019, vol. 23, no. 10, p. 1836–1839. DOI: 10.1109/LCOMM.2019.2933211
  9. ZHAO, W., ZHANG, H., ZOU, J., et al. Effective chirp modulation communication system design for LEO satellite IoT. IEEE Internet of Things Journal, 2025, vol. 12, no. 11, p. 16962–16976. DOI: 10.1109/JIOT.2025.3533484
  10. DONG, X., ZHAO, Z., WANG, Y., et al. FMCW radar-based hand gesture recognition using spatiotemporal deformable and context aware convolutional 5-D feature representation. IEEE Transactions on Geoscience and Remote Sensing, 2022, vol. 60, p. 1–11. DOI: 10.1109/TGRS.2021.3122332
  11. SLESICKA, A., KAWALEC, A. Real-time hand gesture recognition for IoT devices using FMCW mmWave radar and continuous wavelet transform. Electronics, 2026, vol. 15, no. 2, p. 1–24. DOI: 10.3390/electronics15020250
  12. GEORGE, S., SEN, P., CARTA, C. Realizing joint communication and sensing RF receiver front–ends: A survey. IEEE Access, 2024, vol. 12, p. 9440–9457. DOI: 10.1109/ACCESS.2024.3351572
  13. YOON, C., CHA, H. Experimental analysis of IEEE 802.15.4a CSS ranging and its implications. Computer Communications, 2011, vol. 34, p. 1361–1374. DOI: 10.1016/j.comcom.2011.02.002
  14. IEEE COMPUTER SOCIETY. IEEE Standard 802.15.4a–2007. New York, NY, USA, Aug. 2007.
  15. HISCOCK, P. D. Chirp Communications. U.S. patent US8718117B2, May 6, 2014.
  16. KIRASAMUTHRANON, L., WARDKEIN, P., KOSEEYAPORN, J. Coding and coherent decoding techniques for continuous single slope cyclic shift chirp signal. Radioengineering, 2023, vol. 32, no. 2, p. 273–286. DOI: 10.13164/re.2023.0273
  17. VANGELISTA, L. Frequency shift chirp modulation: The LoRa modulation. IEEE Signal Processing Letters, 2017, vol. 24, no. 12, p. 1818–1821. DOI: 10.1109/LSP.2017.2762960
  18. TANG, X., LI, H., ZHANG, Y., et al. Performance analysis of LoRa modulation with residual frequency offset. In The 4th IEEE International Conference on Computer and Communications (ICCC). Chengdu (China), 2018, p. 835–839. DOI: 10.1109/CompComm.2018.8780636
  19. LI, H., ZHANG, Y., ZHAO, X., et al. Demodulation of frequency shift chirp spreading spectrum with cyclic prefix. In The 15th IEEE International Conference on Signal Processing (ICSP). Beijing (China), 2020, p. 433–438. DOI: 10.1109/ICSP48669.2020.9320951
  20. KIRASAMUTHRANON, L., WARDKEIN, P., KOSEEYAPORN, J. Novel double slope frequency shift chirp symbol. IEEE Access. 2024, vol. 12, p. 83417–83426.
  21. DOI: 10.1109/ACCESS.2024.3406218
  22. FIMANSYAH, I., YAMAGUCHI, Y. FPGA-based implementation of a chirp signal generator using an OpenCL design. Microprocessors and Microsystems Journal, 2020, vol. 77, p. 1–8. DOI: 10.1016/j.micpro.2020.103199
  23. CHUA, M. Y., KOO, V. C. FPGA-based chirp generator for high resolution UAV SAR. Progress in Electromagnetics Research (PIER), 2009, vol. 99, p. 71–88. DOI: 10.2528/PIER09100301
  24. FIRMANSYAH, I., ADITYA, S. A., SETIADI, B., et al. FPGA based implementation of I/Q chirp signal generator using memory based technique. In Proceedings of the International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET). Bandung (Indonesia), 2022, p. 78–82. DOI: 10.1109/ICRAMET56917.2022.9991231
  25. AL–DUJAILI, M. J., AL–AWADI, A. M. Chirplet signal design by FPGA. International Journal of Electrical and Computer Engineering (IJECE), 2021, vol. 11, no. 3, p. 2120–2127. DOI: 10.11591/ijece.v11i3.pp2120-2127
  26. ALSHAREF, M., HAMED, A. M., RAO, K. R. Error rate performance of digital chirp communication system over fading channels. In Proceedings of the World Congress on Engineering and Computer Science (WCECS). San Francisco (USA), 2015, vol. II, p. 1–6. ISBN: 978-988-14047-2-5
  27. PROAKIS, J. G., SALEHI, M. Digital Communications. 5th ed., New York (USA): McGraw-Hill, 2008, p. 830–898. ISBN: 978–0–07–295716–7
  28. SIMON, M. K., ALOUINI, M.-S. Digital Communication over Fading Channels. 2nd ed. New York (NY, USA): Wiley, 2005. Ch. 5 Useful Expressions for Evaluating Average Error Probability Performance, p. 123–167. DOI: 10.1002/0471715220
  29. KHAN, M. A., RAO, R. K., WANG, X. Performance analysis of MRC–chirp system over independent and correlated fading channels. In Proceedings of the 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). Regina (SK, Canada), 2013, p. 1–4. DOI: 10.1109/CCECE.2013.6567753
  30. EL-KHAMY, S., SHAABAN, S., THABET, E. A. Frequency hopped multi-user chirp modulation (FH/M-CM) for multipath fading channels. In Proceedings of the 16th National Radio Science Conference (NRSC’99). Cairo (Egypt), 1999, vol. 2, p. c6(1)–c6(8). DOI: 10.1109/NRSC.1999.760890
  31. HAMED, A.M., ALSHAREF, M., RAO, K. R. Bit error probability performance bounds of CPFSK over fading channels. In Proceedings of the 28th Canadian Conference on Electrical and Computer Engineering (CCECE). Halifax (NS, Canada), 2015, p. 1329–1334. DOI: 10.1109/CCECE.2015.7129471
  32. KIM, S.-G., LEE, H.-K., KIM, K. Performance analysis of MTCM/CPFSK in correlated Rayleigh fading channels. Electronics Letters, 2002, vol. 38, no. 22, p. 1367–1368. DOI: 10.1049/el:20020955

Keywords: Double-slope chirp, chirp encoding, asynchronous decoding, data communication

X. Yao Z. Xu, G. Liu [references] [full-text] [DOI: 10.13164/re.2026.0232] [Download Citations]
UWB Indoor Localization Based on XGBoost NLOS Identification and DS-TWR Ranging

Indoor environments present significant challenges for ultra-wideband (UWB) localization due to ranging errors and non-line-of-sight (NLOS) propagation. This paper proposes a robust UWB indoor localization framework that integrates double-sided two-way ranging (DS-TWR), XGBoost-based NLOS identification, residual-weighted localization, and Kalman filter (KF). The main contribution of this work is the unified use of NLOS identification in both ranging correction and localization fusion, significantly improving localization accuracy in complex environments. Experimental results demonstrate improvements in ranging accuracy of up to 53.7% and 47.22% under human-body and wooden-board occlusions. In dynamic experiments, the proposed method outperforms conventional UWB localization, KF, and weighted least squares methods with positioning accuracy improvements of 38.64%, 28.95%, and 12.9%, respectively. These results confirm the framework’s effectiveness in mitigating NLOS impact and enhancing UWB localization robustness.

  1. SVECOVA, M., KOCUR, D. Time of arrival complementing method for cooperative localization of a target by two-node UWB sensor network. Radioengineering, 2016, vol. 25, no. 3, p. 602–611. DOI: 10.13164/re.2016.0602
  2. MUQAIBEL, A. H., ALAWSH, S. A., BINMAKHASHEN, G. M. Under-sampled UWB NLOS/LOS channel classification using machine learning. Arabian Journal for Science and Engineering, 2024, vol. 50, no. 8, p. 6095–6108. DOI: 10.1007/s13369-02409785-x
  3. SANTAMARIA-PEDRON, J. C., BERKVENS, R., MIRALLES, I., et al. Machine learning integration in ultra-wideband-based indoor positioning systems: A comprehensive review. Electronics (Basel), 2025, vol. 15, no. 1, p. 1–55. DOI: 10.3390/electronics15010181
  4. MAJEED, A. F., ARSAT, R., BAHARUDIN, M. A., et al. Accurate multiclass NLOS channels identification in UWB indoor positioning system-based deep neural network. IEEE Access, 2024, vol. 12, p. 179431–179448. DOI: 10.1109/ACCESS.2024.3509343
  5. REN, H. Y., GUO, C. X., YANG, R. F. Research on improving UWB ranging accuracy by Kalman filter (in Chinese). Electronic Measurement Technology, 2024, vol. 44, no. 18, p. 111-115.
  6. CHEN, S. Y., YIN, D., NIU, Y.F. Research and implementation of improved SS-TWR method based on UWB (in Chinese). Application Research of Computers, 2023, vol. 38, no. 11, p. 3398 to 3402. DOI: 10.19734/j.issn.1001-3695.2021.04.0116
  7. TIEN LE MINH, DUNG TRINH XUAN Applying Kalman filter to UWB positioning with DS-TWR method in LOS/NLOS scenarios. In 2021 International Symposium on Electrical and Electronics Engineering (ISEE). Ho Chi Minh City (Vietnam), 2021, p. 95–99. DOI: 10.1109/ISEE51682.2021.9418707
  8. WANG, G., LI, S., CHENG, P., et al. ToF-based NLoS indoor tracking with adaptive ranging error mitigation. IEEE Transactions on Signal Processing, 2024, vol. 72, p. 4855–4870. DOI: 10.1109/TSP.2024.3468467
  9. GUO, A. J. Joint positioning method of PDOA and TOF in coal mines based on UWB (in Chinese). Journal of Mine Automation, 2023, vol. 49, no. 3, p. 137–141. DOI: 10.13272/j.issn.1671251x.18078
  10. GUVENC, I., CHONG, C. C., WATANABE F., et al. NLOS identification and weighted least-squares localization for UWB systems using multipath channel statistic. EURASIP Journal on Advances in Signal Processing, 2007, no. 1, p. 1–14. DOI: 10.1155/2008/271984
  11. MARANO, S., GIFFORD, W. M., WYMEESCH, H., et al. NLOS identification and mitigation for localization based on UWB experimental data. IEEE Journal on Selected Areas in Communications, 2010, vol. 28, no. 7, p. 1026–1035. DOI: 10.1109/JSAC.2010.100907
  12. DJOSIC, S., STOJANOVIC, I., JOVANOVIC, M., et al. Multialgorithm UWB-based localization method for mixed LOS/NLOS environments. Computer Communications, 2022, vol. 181, p. 365 to 373. DOI: 10.1016/j.comcom.2021.10.031
  13. XIAO, N., SHI, S. A real-time identification and suppression algorithm of non-line-of-sight error based on TOA (in Chinese). Guangdong Communication Technology, 2020, vol. 40, no. 5, p. 67–70.
  14. ZENG, L., PENG, C., LIU, H. UWB indoor positioning algorithm based on NLOS identification (in Chinese). Computer Applications, 2018, vol. 36, no. 6, p. 1557–1561.
  15. PARK, J. W., NAM, S. C., CHOI, H. B., et al. Improving deep learning-based UWB LOS/NLOS identification with transfer learning: An empirical approach. Electronics, 2020, vol. 9, no. 10, p. 1–13. DOI: 10.3390/electronics9101714
  16. CORTESI, S., DREHER, M., MAGNO, M. Design and implementation of an RSSI-based Bluetooth low energy indoor localization system. In 17th International Conference on Wireless and Mobile Computing, Networking and Communications. 2021, p. 163–168. DOI: 10.1109/WiMob52687.2021.9606272
  17. YAO, X. L., XU, Z. J., QIANG, F. High-precision indoor localization via dual-modal AOA/TOA fusion with deep learning and particle filters. Radioengineering, 2025, vol. 34, no. 4. p. 624 to 640. DOI: 10.13164/re.2025.0624
  18. GUVENC, I., CHONG, C. C. A survey on TOA based wireless localization and NLOS mitigation techniques. IEEE Communications Surveys & Tutorials, 2009, vol. 11, no. 3. p. 107 to 124. DOI: 10.1109/SURV.2009.090308
  19. LIU, Q. L, WAN, Z. P., ZHOU, W. M., et al. An improved indoor localization method based on multi-source information fusion (in Chinese). Telecommunications Technology, 2021, vol. 61, no. 12, p. 1526–1533.
  20. WANG, W., HUANG, J., CAI, S., et al. Design and implementation of synchronization-free TDOA localization system based on UWB. Radioengineering, 2019, vol. 27, no. 1, p. 320–330. DOI: 10.13164/re.2019.0320
  21. ZHANG, D., XU, H. B., ZHAN, L., et al. Accurate joint estimation of position and orientation based on angle of arrival and two-way ranging of ultra-wideband technology. Electronics, 2025, vol. 14, no. 3, p. 1–17. DOI: 10.3390/electronics14030429
  22. LIU, M. Q. NLOS Identification and Error Reduction Method for Ultra-Wideband Positioning. Diploma Theses. Jiangsu, China University of Mining and Technology, 2023.
  23. GUAN, B., WANG, D. H., SHU, D., et al. Data-driven casting defect prediction model for sand casting based on random forest classification algorithm. China Foundry, 2024, vol. 21, no. 2, p. 137–146. DOI: 10.1007/s41230-024-3090-1
  24. YANG, S., CHOU, L., LI, H., et al. Hierarchical self-distillation with attention for class-imbalanced acoustic event classification in elevators. Sensors, 2026, vol. 26, no. 2, p. 1–27. DOI: 10.3390/s26020589
  25. KHODARAHMI, M., MAIHAMI, V. A review on Kalman filter models. Archives of Computational Methods in Engineering, 2023, vol. 30, no. 1, p. 727–747. DOI: 10.1007/s11831-022-09815-7

Keywords: Ultra-Wide Band (UWB), indoor localization, Non-Line-of-Sight (NLOS) identification, Double-Sided Two-Way Ranging (DS-TWR), XGBoost algorithm