Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 154

The DOI matches its Crossref record.

Recent Advances on Jamming and Spoofing Detection in GNSS

Radoš, Katarina, Brkić, Marta, Begušić, Dinko·2024·Sensors
DOI 10.3390/s24134210
Open this paper in CoChat — free account

Abstract

Increased interest in the development and integration of navigation and positioning services into a wide range of receivers makes them susceptible to a variety of security attacks such as Global Navigation Satellite Systems (GNSS) jamming and spoofing attacks. The availability of low-cost devices including software-defined radios (SDRs) provides a wide accessibility of affordable platforms that can be used to perform these attacks. Early detection of jamming and spoofing interferences is essential for mitigation and avoidance of service degradation. For these reasons, the development of efficient detection methods has become an important research topic and a number of effective methods has been reported in the literature. This survey offers the reader a comprehensive and systematic review of methods for detection of GNSS jamming and spoofing interferences. The categorization and classification of selected methods according to specific parameters and features is provided with a focus on recent advances in the field. Although many different detection methods have been reported, significant research efforts toward developing new and more efficient methods remain ongoing. These efforts are driven by the rapid development and increased number of attacks that pose high-security risks. The presented review of GNSS jamming and spoofing detection methods may be used for the selection of the most appropriate solution for specific purposes and constraints and also to provide a reference for future research.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s24134210

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 95

  1. Psiaki, 2016, Attackers can spoof navigation signals without our knowledge. Here is how to fight back GPS lies, IEEE Spectr., 53, 26, 10.1109/MSPEC.2016.7524168 · DOI
  2. Wu, 2020, Spoofing and Anti-Spoofing Technologies of Global Navigation Satellite System: A Survey, IEEE Access, 8, 165444, 10.1109/ACCESS.2020.3022294 · DOI
  3. Humphreys, T.E., Ledvina, B.M., Psiaki, M.L., O’Hanlon, B.W., and Kintner, P.M. (2008, January 16–19). Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer. Proceedings of the 21st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS Conference), Savannah, GA, USA.
  4. Humphreys, 2013, Detection Strategy for Cryptographic GNSS Anti-Spoofing, IEEE Trans. Aerosp. Electron. Syst., 49, 1073, 10.1109/TAES.2013.6494400 · DOI
  5. Meng, L., Yang, L., Yang, W., and Zhang, L. (2022). A Survey of GNSS Spoofing and Anti-Spoofing Technology. Remote Sens., 14. · DOI
  6. Turner, M., Wimbush, S., Enneking, C., and Konovaltsev, A. (2020, January 20–23). Spoofing Detection by Distortion of the Correlation Function. Proceedings of the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA. · DOI
  7. Khoei, T.T., Gasimova, A., Ahajjam, M.A., Shamaileh, K.A., Devabhaktuni, V., and Kaabouch, N. (2022, January 19–21). A Comparative Analysis of Supervised and Unsupervised Models for Detecting GPS Spoofing Attack on UAVs. Proceedings of the 2022 IEEE International Conference on Electro Information Technology (eIT), Mankato, MN, USA. · DOI
  8. Shafique, 2021, Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models, IEEE Access, 9, 93803, 10.1109/ACCESS.2021.3089847 · DOI
  9. Gallardo, 2020, SCER Spoofing Attacks on the Galileo Open Service and Machine Learning Techniques for End-User Protection, IEEE Access, 8, 85515, 10.1109/ACCESS.2020.2992119 · DOI
  10. Semanjski, S., Semanjski, I., De Wilde, W., and Gautama, S. (2020). Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part II. Sensors, 20. · DOI
  11. Chen, 2022, GNSS Multiparameter Spoofing Detection Method Based on Support Vector Machine, IEEE Sens. J., 22, 17864, 10.1109/JSEN.2022.3193388 · DOI
  12. Radoš, K., Brkić, M., and Begušić, D. (2024, January 20–24). GNSS Signal Classification based on Machine Learning Methods. Proceedings of the 2024 47th MIPRO ICT and Electronics Convention (MIPRO), Opatija, Croatia. accepted. · DOI
  13. Semanjski, S., Semanjski, I., De Wilde, W., and Muls, A. (2020). Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I. Sensors, 20. · DOI
  14. Elango, A., Ujan, S., and Ruotsalainen, L. (2022, January 7–9). Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach. Proceedings of the 2022 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland. · DOI
  15. Li, 2024, Detecting GNSS spoofing using deep learning, EURASIP J. Adv. Signal Process., 2024, 14, 10.1186/s13634-023-01103-1 · DOI
  16. Marchand, M., Toumi, A., Seco-Granados, G., and López-Salcedo, J.A. (2023, January 6–8). Machine Learning Assessment of Anti-Spoofing Techniques for GNSS Receivers. Proceedings of the WIPHAL 2023: Work-in-Progress in Hardware and Software for Location Computation, CEUR Workshop Proceedings, Castellon, Spain.
  17. Truong, V., Vervisch-Picois, A., Rubio Hernan, J., and Samama, N. (2023). Characterization of the Ability of Low-Cost GNSS Receiver to Detect Spoofing Using Clock Bias. Sensors, 23. · DOI
  18. Yang, Q., and Chen, Y. (2022, January 25–27). A GPS Spoofing Detection Method Based on Compressed Sensing. Proceedings of the 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xi’an, China. · DOI
  19. Lee, D.-K., Miralles, D., Akos, D., Konovaltsev, A., Kurz, L., Lo, S., and Nedelkov, F. (2020, January 23–24). Detection of GNSS Spoofing using NMEA Messages. Proceedings of the European Navigation Conference (ENC), Dresden, Germany. · DOI
  20. Li, J., Li, W., He, S., Dai, Z., and Fu, Q. (2020, January 8–12). Research on Detection of Spoofing Signal with Small Delay Based on KNN. Proceedings of the 2020 IEEE 3rd International Conference on Electronics Technology (ICET), Chengdu, China. · DOI
  21. Broumandan, 2012, GPS vulnerability to spoofing threats and a review of antispoofing techniques, Int. J. Navig. Observ., 2012, 127072
  22. Lee, Y.-S., Yeom, J.S., and Jung, B.C. (2023, January 8–11). A Novel Array Antenna-Based GNSS Spoofing Detection and Mitigation Technique. Proceedings of the 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA. · DOI
  23. Zhang, Z., and Zhan, X. (2016). GNSS Spoofing Network Monitoring Based on Differential Pseudorange. Sensors, 16. · DOI
  24. Liu, K., Wu, W., Wu, Z., He, L., and Tang, K. (2018). Spoofing Detection Algorithm Based on Pseudorange Differences. Sensors, 18. · DOI
  25. Wang, W., Aguilar Sanchez, I., Caparra, G., McKeown, A., Whitworth, T., and Lohan, E.S. (2021). A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data. Sensors, 21. · DOI
  26. Morales-Ferre, R., Wang, W., Sanz-Abia, A., and Lohan, E.-S. (2020). Identifying GNSS Signals Based on Their Radio Frequency (RF) Features—A Dataset with GNSS Raw Signals Based on Roof Antennas and Spectracom Generator. Data, 5. · DOI
  27. Wang, W., Lohan, E.S., Sanchez, I.A., and Caparra, G. (2022, January 5–7). Pre-correlation and post-correlation RF fingerprinting methods for GNSS spoofer identification with real-field measurement data. Proceedings of the 10th Workshop on Satellite Navigation Technology (NAVITEC), Noordwijk, The Netherlands. · DOI
  28. Zhang, 2023, Noise-Like Features-Assisted GNSS Spoofing Detection Based on Convolutional Autoencoder, IEEE Sens. J., 23, 25473, 10.1109/JSEN.2023.3311799 · DOI
  29. Li, X., Chen, L., Lu, Z., Wang, F., Liu, W., Xiao, W., and Liu, P. (2023). Overview of Jamming Technology for Satellite Navigation. Machines, 11. · DOI
  30. Songala, K.K., Ammana, S.R., Ramachandruni, H.C., and Achanta, D.S. (2020, January 26–27). Simplistic Spoofing of GPS Enabled Smartphone. Proceedings of the 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India. · DOI
  31. Radoš, K., Brkić, M., and Begušić, D. (2024, January 20–24). Vulnerability of Smartphones on GNSS Simplistic Spoofing Attack. Proceedings of the 2024 47th MIPRO ICT and Electronics Convention (MIPRO), Opatija, Croatia. accepted. · DOI
  32. Psiaki, 2016, GNSS Spoofing and Detection, Proc. IEEE, 104, 1258, 10.1109/JPROC.2016.2526658 · DOI · Paper page
  33. Garbin Manfredini, E. (2017). Signal Processing Techniques for GNSS Anti-Spoofing Algorithms. [Ph.D. Thesis, Polytechnic University of Turin].
  34. Broumandan, A., Kennedy, S., and Schleppe, J. (2020, January 20–23). Demonstration of a Multi-Layer Spoofing Detection Implemented in a High Precision GNSS Receiver. Proceedings of the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA. · DOI
  35. Li, 2021, GNSS Spoofing Jamming Detection Based on Generative Adversarial Network, IEEE Sens. J., 21, 22823, 10.1109/JSEN.2021.3105404 · DOI
  36. Yang, 2022, Research on GNSS Spoofing Mitigation Technology Based on Spoofing Correlation Peak Cancellation, IEEE Commun. Lett., 26, 3024, 10.1109/LCOMM.2022.3204944 · DOI
  37. Fang, 2023, A post-correlation graphical way for continuous GNSS spoofing detection, Measurement, 216, 112974, 10.1016/j.measurement.2023.112974 · DOI
  38. Zhou, 2024, Detection of Spoofing Attacks on Global Navigation Satellite Systems Using Kolmogorov–Smirnov Test-Based Signal Quality Monitoring Method, IEEE Sens. J., 24, 10474, 10.1109/JSEN.2024.3354110 · DOI
  39. Wang, J., Tang, X., Ma, P., Wu, J., Ma, C., and Sun, G. (2023). GNSS Spoofing Detection Using Q Channel Energy. Remote Sens., 15. · DOI
  40. Yakkati, R.R., Pardhasaradhi, B., Zhou, J., and Cenkeramaddi, L.R. (2022, January 18–22). A Machine Learning based GNSS Signal Classification. Proceedings of the 2022 IEEE International Symposium on Smart Electronic Systems (iSES), Warangal, India. · DOI
  41. Zidan, 2021, GNSS Vulnerabilities and Existing Solutions: A Review of the Literature, IEEE Access, 9, 153960, 10.1109/ACCESS.2020.2973759 · DOI
  42. Broumandan, 2012, GPS spoofer countermeasure effectiveness based on using signal strength noise power and C/N0 observables, Int. J. Satellite Commun. Netw., 30, 181, 10.1002/sat.1012 · DOI
  43. Zhu, X., Lu, Z., Hua, T., Yang, F., Tu, G., and Chen, X. (2022). A Novel GPS Meaconing Spoofing Detection Technique Based on Improved Ratio Combined with Carrier-to-Noise Moving Variance. Electronics, 11. · DOI
  44. Pardhasaradhi, 2022, Machine Learning-Based Screening and Measurement to Measurement Association for Navigation in GNSS Spoofing Environment, IEEE Sens. J., 22, 23423, 10.1109/JSEN.2022.3214349 · DOI
  45. Magiera, J. (2019). A Multi-Antenna Scheme for Early Detection and Mitigation of Intermediate GNSS Spoofing. Sensors, 19. · DOI
  46. Zhao, 2022, A Coprime Array-Based Technique for Spoofing Detection and DoA Estimation in GNSS, IEEE Sens. J., 22, 22828, 10.1109/JSEN.2022.3211024 · DOI
  47. Chen, J., Wang, X., Fang, Z., Jiang, C., Gao, M., and Xu, Y. (2024). A Real-Time Spoofing Detection Method Using Three Low-Cost Antennas in Satellite Navigation. Electronics, 13. · DOI
  48. Yang, H., Jin, R., Xu, W., Che, L., and Zhen, W. (2023). Satellite Navigation Spoofing Interference Detection and Direction Finding Based on Array Antenna. Sensors, 23. · DOI
  49. Meurer, M., Konovaltsev, A., Appel, M., and Cuntz, M. (2016, January 25–28). Direction-of-Arrival Assisted Sequential Spoofing Detection and Mitigation. Proceedings of the 2016 International Technical Meeting of the Institute of Navigation, Monterey, CA, USA. · DOI
  50. Liu, R., Yang, Z., Chen, Q., Liao, G., and Zhu, Q. (2023). Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna. Remote Sens., 15. · DOI
  51. Zhang, 2018, Statistical analysis of spoofing detection based on TDOA, IEEJ Trans. Electr. Electron. Eng., 13, 840, 10.1002/tee.22637 · DOI
  52. Mao, P., Yuan, H., Chen, X., Gong, Y., Li, S., Li, R., Luo, R., Zhao, G., Fu, C., and Xu, J. (2023). A GNSS Spoofing Detection and Direction-Finding Method Based on Low-Cost Commercial Board Components. Remote Sens., 15. · DOI
  53. Chen, S., Ni, S., Lei, T., Cheng, L., and Song, X. (2024). GNSS Spoofing Detection via the Intersection Angle between Two Directions of Arrival in a Single Rotating Antenna. Sensors, 24. · DOI
  54. Chang, H., Pang, C., Zhang, L., and Guo, Z. (2022). Rotating Single-Antenna Spoofing Signal Detection Method Based on IPNN. Sensors, 22. · DOI
  55. Xie, 2022, Localizing GNSS Spoofing Attacks Using Direct Position Determination, IEEE Sens. J., 22, 15323, 10.1109/JSEN.2022.3179557 · DOI
  56. Spravil, J., Hemminghaus, C., von Rechenberg, M., Padilla, E., and Bauer, J. (2023). Detecting Maritime GPS Spoofing Attacks Based on NMEA Sentence Integrity Monitoring. J. Mar. Sci. Eng., 11. · DOI
  57. Xiao, L., Li, X., and Wang, G. (2019, January 19–20). GNSS Spoofing Detection Using Pseudo-range Double Differences between Two Receivers. Proceedings of the 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China. · DOI
  58. Semanjski, S., Muls, A., Semanjski, I., and De Wilde, W. (2019, January 4–6). Use and Validation of Supervised Machine Learning Approach for Detection of GNSS Signal Spoofing. Proceedings of the 2019 International Conference on Localization and GNSS (ICL-GNSS), Nuremberg, Germany. · DOI
  59. Siemuri, 2022, A Systematic Review of Machine Learning Techniques for GNSS Use Cases, IEEE Trans. Aerosp. Electron. Syst., 58, 5043, 10.1109/TAES.2022.3219366 · DOI
  60. Aissou, G., Slimane, H.O., Benouadah, S., and Kaabouch, N. (2021, January 1–4). Tree-based Supervised Machine Learning Models for Detecting GPS Spoofing Attacks on UAS. Proceedings of the 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA. · DOI
  61. Aissou, G., Benouadah, S., El Alami, H., and Kaabouch, N. (2022, January 26–29). Instance-based Supervised Machine Learning Models for Detecting GPS Spoofing Attacks on UAS. Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA. · DOI
  62. Rossouw van der Merwe, J., Nikolikj, A., Kram, S., Lukcin, I., Nadzinski, G., Rügamer, A., and Felber, W. (2020, January 21–25). Blind Spoofing Detection for Multi-Antenna Snapshot Receivers using Machine-Learning Techniques. Proceedings of the 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+2020), Online.
  63. Rustamov, A., Gogoi, N., Minetto, A., and Dovis, F. (2020, January 2–4). Assessment of the Vulnerability to Spoofing Attacks of GNSS Receivers Integrated in Consumer Devices. Proceedings of the 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland. · DOI
  64. Liu, 2023, Robust Spoofing Detection for GNSS Array Instrumentation Based on C/N0 Difference Measurements, IEEE Trans. Instrum. Meas., 72, 8507211, 10.1109/TIM.2023.3328684 · DOI
  65. Huang, L., and Yang, Q. (2015, January 6–9). Low-cost GPS simulator GPS spoofing by SDR. Proceedings of the DEFCON, Las Vegas, NV, USA.
  66. Rustamov, 2023, Improving GNSS Spoofing Awareness in Smartphones via Statistical Processing of Raw Measurements, IEEE Open J. Commun. Soc., 4, 873, 10.1109/OJCOMS.2023.3260905 · DOI
  67. Ceccato, S., Formaggio, F., Caparra, G., Laurenti, N., and Tomasin, S. (2018, January 23–26). Exploiting side-information for resilient GNSS positioning in mobile phones. Proceedings of the 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA. · DOI
  68. Guo, C., and Yang, Z. (2023, January 11–15). Robust RF Fingerprint Extraction Scheme for GNSS Spoofing Detection. Proceedings of the 36th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2023), Denver, CO, USA. · DOI
  69. Humphreys, T.E., Bhatti, J.A., Shepard, D.P., and Wesson, K.D. (2012, January 17–21). The Texas Spoofing Test Battery: Toward a Standard for Evaluating GPS Signal Authentication Techniques. Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2012), Nashville, TN, USA.
  70. Manfredini, E.G., Akos, D.M., Chen, Y.-H., Lo, S., Walter, T., and Enge, P. (February, January 29). Effective GPS Spoofing Detection Utilizing Metrics from Commercial Receivers. Proceedings of the 2018 International Technical Meeting of The Institute of Navigation, Reston, VA, USA. · DOI
  71. Spens, N., Lee, D.-K., Nedelkov, F., and Akos, D. (2022). Detecting GNSS Jamming and Spoofing on Android Devices. NAVIGATION J. Inst. Navig. Sept., 69. · DOI
  72. Spens, N., Lee, D.-K., and Akos, D. (2021, January 20–24). An application for detecting GNSS jamming and spoofing. Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+), St. Louis, MO, USA. · DOI
  73. Zhang, J., Cui, X., Xu, H., and Lu, M. (2019). A Two-Stage Interference Suppression Scheme Based on Antenna Array for GNSS Jamming and Spoofing. Sensors, 19. · DOI
  74. Hu, 2018, A Novel Array-Based Spoofing and Jamming Suppression Method for GNSS Receiver, IEEE Sens. J., 18, 2952, 10.1109/JSEN.2018.2797309 · DOI
  75. Morales Ferre, R., de la Fuente, A., and Lohan, E.S. (2019). Jammer Classification in GNSS Bands via Machine Learning Algorithms. Sensors, 19. · DOI
  76. Shafiee, 2018, Detection of Spoofing Attack using Machine Learning based on Multi-Layer Neural Network in Single-Frequency GPS Receivers, J. Navig., 71, 169, 10.1017/S0373463317000558 · DOI
  77. Kartchner, D.R., Palmer, R., and Jayaweera, S.K. (2021, January 21–23). Satellite navigation anti-spoofing using deep learning on a receiver network. Proceedings of the 2021 IEEE Cognitive Communications for Aerospace Applications Workshop, Cleveland, OH, USA. · DOI
  78. Li, W., Huang, Z., Lang, R., Qin, H., Zhou, K., and Cao, Y. (2016). A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method. Sensors, 16. · DOI
  79. Wu, Z., Zhao, Y., Yin, Z., and Luo, H. (2017, January 18–20). Jamming signals classification using convolutional neural network. Proceedings of the 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain. · DOI
  80. Alkhatib, M., McCormick, M., Williams, L., leon, A., Camerano, L., Al Shamaileh, K., Devabhaktuni, V., and Kaabouch, N. (2024, January 5–8). Classification and Source Location Indication of Jamming Attacks Targeting UAVs via Multi-output Multiclass Machine Learning Modeling. Proceedings of the 2024 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA. · DOI
  81. Wu, P., Calatrava, H., Imbiriba, T., and Closas, P. (2023, January 24–27). Jammer classification with Federated Learning. Proceedings of the 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA. · DOI
  82. Swinney, C.J., and Woods, J.C. (2021, January 14–18). GNSS Jamming Classification via CNN, Transfer Learning & the Novel Concatenation of Signal Representations. Proceedings of the 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), Dublin, Ireland. · DOI
  83. Miralles, D., Levigne, N., Akos, D.M., Blanch, J., and Lo, S. (2018, January 24–28). Android Raw GNSS Measurements as the New Anti-Spoofing and Anti-Jamming Solution. Proceedings of the 31st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2018), Miami, FL, USA. · DOI
  84. Scott, L. (2011, January 19–23). J911: The case for fast jammer detection and location using crowdsourcing approaches. Proceedings of the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2011), Portland, OR, USA.
  85. Axell, 2015, Jamming Detection in GNSS Receivers: Performance Evaluation of Field Trials, J. Inst. Navig., 62, 73, 10.1002/navi.74 · DOI
  86. Strizic, L., Akos, D.M., and Lo, S. (February, January 29). Crowdsourcing GNSS Jammer Detection and Localization. In Proceedings of the 2018 International Technical Meeting of the Institute of Navigation, Reston, VA, USA. · DOI
  87. Olsson, G.K., Axell, E., Larsson, E.G., and Papadimitratos, P. (2022, January 7–9). Participatory Sensing for Localization of a GNSS Jammer. Proceedings of the 2022 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland. · DOI
  88. Olsson, G.K., Nilsson, S., Axell, E., Larsson, E.G., and Papadimitratos, P. (2023, January 24–27). Using Mobile Phones for Participatory Detection and Localization of a GNSS Jammer. Proceedings of the 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA. · DOI
  89. Yozevitch, R., Marbel, R., Flysher, N., and Ben-Moshe, B. (2021). Save Our Roads from GNSS Jamming: A Crowdsource Framework for Threat Evaluation. Sensors, 21. · DOI
  90. Osman, 2020, DOA Elevation and Azimuth Angles Estimation of GPS Jamming Signals Using Fast Orthogonal Search, IEEE Trans. Aerosp. Electron. Syst., 56, 3812, 10.1109/TAES.2020.2988424 · DOI
  91. Morales Ferre, R., Richter, P., De La Fuente, A., and Lohan, E.S. (2019, January 4–6). In-lab validation of jammer detection and direction finding algorithms for GNSS. Proceedings of the 2019 International Conference on Localization and GNSS (ICL-GNSS), Nuremberg, Germany. · DOI
  92. Thanakan, K., Sapphaniran, K., Palasarn, T., Supnithi, P., Phakphisut, W., and Sakorn, C. (2021, January 19–22). Real-Time Jamming Detection and Position Estimation via Software-Defined Radio (SDR). Proceedings of the 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Mai, Thailand. · DOI
  93. Medina, D., Lass, C., Marcos, E.P., Ziebold, R., Closas, P., and García, J. (2019, January 2–5). On GNSS Jamming Threat from the Maritime Navigation Perspective. Proceedings of the 2019 22th International Conference on Information Fusion (FUSION), Ottawa, ON, Canada. · DOI
  94. Panice, G., Luongo, S., Gigante, G., Pascarella, D., Di Benedetto, C., Vozella, A., and Pescape, A. (2017, January 7–8). A SVM-based detection approach for GPS spoofing attacks to UAV. Proceedings of the 2017 23rd International Conference on Automation and Computing (ICAC), Huddersfield, UK. · DOI
  95. Tohidi, S., and Mosavi, M.R. (2020, January 1–2). Effective detection of GNSS spoofing attack using a multi-layer perceptron neural network classifier trained by PSO. Proceedings of the 2020 25th International Computer Conference, Computer Society of Iran (CSICC), Teheran, Iran. · DOI

Cited by

See all 154

Metadata

Authors
Radoš, K., Brkić, M., & Begušić, D.
Affiliations
  • Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split, 21000 Split, Croatia
  • Ericsson Nikola Tesla d.d., 10000 Zagreb, Croatia
ORCID
Radoš, Katarina
Venue
Sensors
Publisher
MDPI AG
Published
2024-06-28
Volume
24
Issue
13
Pages
4210
ISSN
1424-8220
DOI
10.3390/s24134210
Cited by
154
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Recent Advances on Jamming and Spoofing Detection in GNSS. https://app.cochat.ai/papers/10.3390/s24134210
The paper
Radoš, K., Brkić, M., & Begušić, D. (2024). Recent Advances on Jamming and Spoofing Detection in GNSS. Sensors. https://doi.org/10.3390/s24134210

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.