Fault-Induced Signal Distortion in FMCW Automotive Radar: A Simulation-Based Analysis
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Jan 13, 2026
Sheriff Murtala
Ingyu Lee
Soojung Hur
Gyusang Choi
Abstract
Faults in automotive radar subsystems distort the radar signal and compromise system performance, especially in frequency-modulated continuous wave (FMCW) radar architectures. However, the signal-level consequences of such faults remain underexplored. This paper presents a simulation-based analysis of signal distortions caused by five representative fault behaviors across three critical FMCW radar subsystems: the waveform generator, transmitter, and receiver. We examine the effects of each fault on complex baseband signals and range estimation accuracy, providing both qualitative and quantitative evaluations. The results reveal distinct distortion patterns and demonstrate that range errors and false negatives can occur independently, highlighting the need for diagnostic and fault-aware processing strategies. This work offers a foundational perspective on fault-induced anomalies in radar signal processing and supports the development of more robust FMCW radar systems.
##plugins.themes.bootstrap3.article.details##
Keywords
Fault Injection, FMCW Radar, Autonomous Vehicles, Signal Distortions, Automotive Radar Subsystems
References
Bak, S.-H., & Kim, K. P. (2023). An automl-driven antenna performance prediction model in the autonomous driving radar manufacturing process. KSII Transactions on Internet & Information Systems, 17(12), 3330–3344. doi: 10.3837/tiis.2023.12.006 Burza, R. M. (2024). Overview of radar alignment methods and analysis of radar misalignment’s impact on active safety and autonomous systems. Sensors, 24(15), 4913. doi: 10.3390/s24154913
Darrah, T., L¨ovberg, A., Frank, J., & Quinones-Gruiero, M. (2022). Developing deep learning models for system remaining useful life predictions: Application to aircraft engines. In Proceedings of the phm society conference (Vol. 14).
Fleck, S., May, B., Daniel, G., & Davies, C. (2021). Data driven degradation of automotive sensors and effect analysis. Electronic Imaging, 33, 1–8.
Ginsburg, B. P., Subburaj, K., Samala, S., Ramasubramanian, K., Singh, J., Bhatara, S., . . . Rentala, V. (2018). A multimode 76-to-81ghz automotive radar transceiver with autonomous monitoring. In 2018 ieee international solid-state circuits conference - (isscc) (p. 158- 160).
Hou, X., Yang, J., Deng, B., Xia, L., Zhang, Y., & Zhang, Z. (2018). A key lifetime parameters extraction method for t/r module based on association rule. In 2018 international conference on sensing, diagnostics, prognostics, and control (sdpc) (pp. 165–170). IEEE. doi: 10.1109/SDPC.2018.8664912
Jankiraman, M. (2018). Fmcw radar design. Norwood, MA: Artech House.
Kraus, F., Scheiner, N., Ritter, W., & Dietmayer, K. (2021). The radar ghost dataset – an evaluation of ghost objects in automotive radar data. In 2021 ieee/rsj international conference on intelligent robots and systems (iros) (p. 8570–8577). IEEE Press. doi: 10.1109/IROS51168.2021.9636338
Kulevome, D. K. B., Hong, W., Wang, X., Cobbinah, B. M., Agbley, B. L. Y., & Safder, Q. (2021). System diagnosis framework for sustaining the operational fidelity of a radar system. In 2021 18th international computer conference on wavelet active media technology and information processing (iccwamtip) (pp. 640–644). IEEE. doi: 10.1109/ICCWAMTIP54135.2021.9730812
Liu, B., Bi, X., Gu, L.,Wei, J., & Liu, B. (2022). Application of a bayesian network based on multi-source information fusion in the fault diagnosis of a radar receiver. Sensors, 22(17), 6396. doi: 10.3390/s22176396
MathWorks. (2024a). Fault analyzer toolbox. Available online: https://www.mathworks.com/help/fault-analyzer. (Accessed: Oct. 2025)
MathWorks. (2024b). Radar toolbox. Available Online: https://www.mathworks.com/help/radar. (Accessed: Oct. 2025)
MathWorks. (2024c). Simulink. Available Online: https://www.mathworks.com/help/simulink. (Accessed: Oct. 2025)
Matos, F., Bernardino, J., Dur˜aes, J., & Cunha, J. (2024). A survey on sensor failures in autonomous vehicles: Challenges and solutions. Sensors, 24(16), 5108. Retrieved from https://doi.org/10.3390/s24165108 doi: 10.3390/s24165108
Moghaddam, M. H., Aghdam, S. R., Filippi, A., & Eriksson, T. (2020). Statistical study of hardware impairments effect on mmwave 77 ghz fmcw automotive radar. In 2020 ieee radar conference (radarconf20) (pp. 1–6).
Murtala, S., Hur, S., & Park, Y. (2025, February 5). Ghost object detection in automotive radar dataset: An encodersvm approach. In Proceedings of symposium of the korean institute of communications and information sciences (pp. 1,447 - 1,448). South Korea. Murtala, S., Lee, I., & Park, Y. (2023).
Identification of key parameters for remaining useful life prediction of radar t/r module using least-squares method. In Phm society european conference (pp. 86–89). Paris, France: PHM Society.
Murtala, S., Soojung, H., & Park, Y. (2023). A simplified framework for fault prediction in radar transmitter based on vector autoregression model. In Phm society asia-pacific conference (Vol. 4). Tokyo, Japan. doi: https://doi.org/10.36001/phmap.2023.v4i1.3627
Sanches, F. S. (2023). Analog fault simulation in automotive radar 77 ghz circuit for safety requirements (Master thesis). University of Jyv¨askyl¨a, Jyv¨askyl¨a, Finland.
Shafique, R., Rustam, F., Murtala, S., Jurcut, A. D., & Choi, G. S. (2024). Advancing autonomous vehicle safety: Machine learning to predict sensor-related accident severity. IEEE Access, 12, 25933-25948. doi: 10.1109/ACCESS.2024.3366990
Sharif, D., Murtala, S., & Choi, G. S. (2025). A survey of automotive radar misalignment detection techniques. IEEE Access, 13, 123314-123324. doi: 10.1109/ACCESS. 2025.3584454 Skolnik, M. I. (2008). Radar handbook. IEEE Aerospace Electronic Systems Magazine, 23(5), 41–41.
Subburaj, K., Ginsburg, B., Gupta, P., Dandu, K., Samala, S., Breen, D., . . . Rangachari, S. (2018). Monitoring architecture for a 76-81ghz radar front end. In 2018 ieee radio frequency integrated circuits symposium (rfic) (p. 264-267). doi: 10.1109/RFIC.2018.8429026
Tang, X., Liu, Z., Liang, J., Wu, K., Bu, Z., & Chen, L. (2023). A fast fault diagnosis method for rf front-end modules based on adaptive signal decomposition and deep neural network. In 2023 ieee autotestcon (pp. 1–5). IEEE. doi: 10.1109/AUTOTESTCON47464.2023.10296419
Tang, X., Liu, Z., Liang, Y., Lin, Y., Luo, Z., Cheng, Y., & Geng, H. (2025). Ecan: Enhanced-feature extraction and complex asymmetric-convolutional-attention network for intelligent fault diagnosis in mimo system. IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2025.3554872 The MathWorks Inc. (2024).
Matlab. Natick, Massachusetts. Retrieved from https://www.mathworks.com (Version 9.13.0 (R2024b)) Wileman, A. J., & Perinpanayagam, S. (2013). Failure mechanisms of radar and rf systems. Procedia CIRP, 11, 56–61. doi: 10.1016/j.procir.2013.07.063
Zhai, Y., & Fang, S. (2020). A degradation fault prognostic method of radar transmitter combining multivariate long short-term memory network and multivariate gaussian distribution. IEEE Access, 8, 199781– 199791. doi: 10.1109/ACCESS.2020.3035622
Zhai, Y., Liu, D., Cheng, Z., & Fang, S. (2022). A novel prognostic model of the degradation malfunction combining a dynamic updated-arima and multivariate isolation forest: Application to radar transmitter. Electronics, 11(12), 1921. doi: 10.3390/electronics1112192
Zhai, Y., Shao, X., Li, J., & Fang, S. (2021). An unsupervised prediction method for radar transmitter degradation fault based on isolation forest. In Journal of physics: Conference series (Vol. 2010, p. 012125). IOP Publishing. doi: 10.1088/1742-6596/2010/1/012125
Z¨oller, M.-A., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. F. (2025). Automated machine learning for remaining useful life predictions. arXiv preprint arXiv:2306.12215. Retrieved from https://arxiv.org/html/2306.12215v2
Darrah, T., L¨ovberg, A., Frank, J., & Quinones-Gruiero, M. (2022). Developing deep learning models for system remaining useful life predictions: Application to aircraft engines. In Proceedings of the phm society conference (Vol. 14).
Fleck, S., May, B., Daniel, G., & Davies, C. (2021). Data driven degradation of automotive sensors and effect analysis. Electronic Imaging, 33, 1–8.
Ginsburg, B. P., Subburaj, K., Samala, S., Ramasubramanian, K., Singh, J., Bhatara, S., . . . Rentala, V. (2018). A multimode 76-to-81ghz automotive radar transceiver with autonomous monitoring. In 2018 ieee international solid-state circuits conference - (isscc) (p. 158- 160).
Hou, X., Yang, J., Deng, B., Xia, L., Zhang, Y., & Zhang, Z. (2018). A key lifetime parameters extraction method for t/r module based on association rule. In 2018 international conference on sensing, diagnostics, prognostics, and control (sdpc) (pp. 165–170). IEEE. doi: 10.1109/SDPC.2018.8664912
Jankiraman, M. (2018). Fmcw radar design. Norwood, MA: Artech House.
Kraus, F., Scheiner, N., Ritter, W., & Dietmayer, K. (2021). The radar ghost dataset – an evaluation of ghost objects in automotive radar data. In 2021 ieee/rsj international conference on intelligent robots and systems (iros) (p. 8570–8577). IEEE Press. doi: 10.1109/IROS51168.2021.9636338
Kulevome, D. K. B., Hong, W., Wang, X., Cobbinah, B. M., Agbley, B. L. Y., & Safder, Q. (2021). System diagnosis framework for sustaining the operational fidelity of a radar system. In 2021 18th international computer conference on wavelet active media technology and information processing (iccwamtip) (pp. 640–644). IEEE. doi: 10.1109/ICCWAMTIP54135.2021.9730812
Liu, B., Bi, X., Gu, L.,Wei, J., & Liu, B. (2022). Application of a bayesian network based on multi-source information fusion in the fault diagnosis of a radar receiver. Sensors, 22(17), 6396. doi: 10.3390/s22176396
MathWorks. (2024a). Fault analyzer toolbox. Available online: https://www.mathworks.com/help/fault-analyzer. (Accessed: Oct. 2025)
MathWorks. (2024b). Radar toolbox. Available Online: https://www.mathworks.com/help/radar. (Accessed: Oct. 2025)
MathWorks. (2024c). Simulink. Available Online: https://www.mathworks.com/help/simulink. (Accessed: Oct. 2025)
Matos, F., Bernardino, J., Dur˜aes, J., & Cunha, J. (2024). A survey on sensor failures in autonomous vehicles: Challenges and solutions. Sensors, 24(16), 5108. Retrieved from https://doi.org/10.3390/s24165108 doi: 10.3390/s24165108
Moghaddam, M. H., Aghdam, S. R., Filippi, A., & Eriksson, T. (2020). Statistical study of hardware impairments effect on mmwave 77 ghz fmcw automotive radar. In 2020 ieee radar conference (radarconf20) (pp. 1–6).
Murtala, S., Hur, S., & Park, Y. (2025, February 5). Ghost object detection in automotive radar dataset: An encodersvm approach. In Proceedings of symposium of the korean institute of communications and information sciences (pp. 1,447 - 1,448). South Korea. Murtala, S., Lee, I., & Park, Y. (2023).
Identification of key parameters for remaining useful life prediction of radar t/r module using least-squares method. In Phm society european conference (pp. 86–89). Paris, France: PHM Society.
Murtala, S., Soojung, H., & Park, Y. (2023). A simplified framework for fault prediction in radar transmitter based on vector autoregression model. In Phm society asia-pacific conference (Vol. 4). Tokyo, Japan. doi: https://doi.org/10.36001/phmap.2023.v4i1.3627
Sanches, F. S. (2023). Analog fault simulation in automotive radar 77 ghz circuit for safety requirements (Master thesis). University of Jyv¨askyl¨a, Jyv¨askyl¨a, Finland.
Shafique, R., Rustam, F., Murtala, S., Jurcut, A. D., & Choi, G. S. (2024). Advancing autonomous vehicle safety: Machine learning to predict sensor-related accident severity. IEEE Access, 12, 25933-25948. doi: 10.1109/ACCESS.2024.3366990
Sharif, D., Murtala, S., & Choi, G. S. (2025). A survey of automotive radar misalignment detection techniques. IEEE Access, 13, 123314-123324. doi: 10.1109/ACCESS. 2025.3584454 Skolnik, M. I. (2008). Radar handbook. IEEE Aerospace Electronic Systems Magazine, 23(5), 41–41.
Subburaj, K., Ginsburg, B., Gupta, P., Dandu, K., Samala, S., Breen, D., . . . Rangachari, S. (2018). Monitoring architecture for a 76-81ghz radar front end. In 2018 ieee radio frequency integrated circuits symposium (rfic) (p. 264-267). doi: 10.1109/RFIC.2018.8429026
Tang, X., Liu, Z., Liang, J., Wu, K., Bu, Z., & Chen, L. (2023). A fast fault diagnosis method for rf front-end modules based on adaptive signal decomposition and deep neural network. In 2023 ieee autotestcon (pp. 1–5). IEEE. doi: 10.1109/AUTOTESTCON47464.2023.10296419
Tang, X., Liu, Z., Liang, Y., Lin, Y., Luo, Z., Cheng, Y., & Geng, H. (2025). Ecan: Enhanced-feature extraction and complex asymmetric-convolutional-attention network for intelligent fault diagnosis in mimo system. IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2025.3554872 The MathWorks Inc. (2024).
Matlab. Natick, Massachusetts. Retrieved from https://www.mathworks.com (Version 9.13.0 (R2024b)) Wileman, A. J., & Perinpanayagam, S. (2013). Failure mechanisms of radar and rf systems. Procedia CIRP, 11, 56–61. doi: 10.1016/j.procir.2013.07.063
Zhai, Y., & Fang, S. (2020). A degradation fault prognostic method of radar transmitter combining multivariate long short-term memory network and multivariate gaussian distribution. IEEE Access, 8, 199781– 199791. doi: 10.1109/ACCESS.2020.3035622
Zhai, Y., Liu, D., Cheng, Z., & Fang, S. (2022). A novel prognostic model of the degradation malfunction combining a dynamic updated-arima and multivariate isolation forest: Application to radar transmitter. Electronics, 11(12), 1921. doi: 10.3390/electronics1112192
Zhai, Y., Shao, X., Li, J., & Fang, S. (2021). An unsupervised prediction method for radar transmitter degradation fault based on isolation forest. In Journal of physics: Conference series (Vol. 2010, p. 012125). IOP Publishing. doi: 10.1088/1742-6596/2010/1/012125
Z¨oller, M.-A., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. F. (2025). Automated machine learning for remaining useful life predictions. arXiv preprint arXiv:2306.12215. Retrieved from https://arxiv.org/html/2306.12215v2
Section
Regular Session Papers
This work is licensed under a Creative Commons Attribution 3.0 Unported License.