journal article Open Access Apr 02, 2025

Advancements in Millimeter-Wave Radar Technologies for Automotive Systems: A Signal Processing Perspective

Electronics Vol. 14 No. 7 pp. 1436 · MDPI AG
View at Publisher Save 10.3390/electronics14071436
Abstract
This review paper provides a comprehensive examination of millimeter-wave radar technologies in automotive systems, reviewing their advancements through signal processing innovations. The evolution of radar systems, from conventional platforms to mmWave technologies, has significantly enhanced capabilities such as high-resolution imaging, real-time tracking, and multi-object detection. Signal processing advancements, including constant false alarm rate detection, multiple-input–multiple-output systems, and machine learning-based techniques, are explored for their roles in improving radar performance under dynamic and challenging environments. The integration of mmWave radar with complementary sensing technologies such as LiDAR and cameras facilitates robust environmental perception essential for advanced driver-assistance systems and autonomous vehicles. This review also calls attention to key challenges, including environmental interference, material penetration, and sensor fusion, while addressing innovative solutions such as adaptive signal processing and sensor integration. Emerging applications of joint communication–radar systems further presents the potential of mmWave radar in autonomous driving and vehicle-to-everything communications. By synthesizing recent developments and identifying future directions, this review stresses the critical role of mmWave radar in advancing vehicular safety, efficiency, and autonomy.
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References
91
[1]
Cressler "SiGe HBT technology: A new contender for Si-based RF and microwave circuit applications" IEEE Trans. Microw. Theory Tech. (1998) 10.1109/22.668665
[2]
Voinigescu, S., Shopov, S., and Chevalier, P. (2015, January 25–27). Millimeter-wave silicon transistor and benchmark circuit scaling through the 2030 ITRS horizon. Proceedings of the Global Symposium on Millimeter-Waves (GSMM), Montreal, QC, Canada. 10.1109/gsmm.2015.7175460
[3]
Krofli "Meteorological research applications of mm-wave radar" Meteorol. Atmos. Phys. (1996) 10.1007/bf01032003
[4]
Meinel, H.H. (1998, January 5–9). Automotive millimeterwave radar history and present status. Proceedings of the 1998 28th European Microwave Conference, Amsterdam, The Netherlands. 10.1109/euma.1998.338059
[5]
Waldschmidt "Automotive radar—From first efforts to future systems" IEEE J. Microw. (2021) 10.1109/jmw.2020.3033616
[6]
Yanovsky, F. (2008). Millimeter-Wave Radar: Principles and Applications. Millimeter Wave Technology in Wireless PAN, LAN, and MAN, Auerbach Publications. 10.1201/9780849382284.ch10
[7]
Yamawaki "Millimeter-wave obstacle detection radar" Fujitsu Tech (2000)
[8]
Zhao "Cubelearn: End-to-end learning for human motion recognition from raw mmwave radar signals" IEEE Internet Things J. (2023) 10.1109/jiot.2023.3237494
[9]
Wei, T., and Zhang, X. (2015, January 7–11). mtrack: High-precision passive tracking using millimeter wave radios. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Paris, France. 10.1145/2789168.2790113
[10]
Huang, X., Tsoi, J.K., and Patel, N. (2022). mmWave radar sensors fusion for indoor object detection and tracking. Electronics, 11. 10.3390/electronics11142209
[11]
Kosuge "mmWave-YOLO: A mmWave imaging radar-based real-time multiclass object recognition system for ADAS applications" IEEE Trans. Instrum. Meas. (2022) 10.1109/tim.2022.3176014
[12]
Gao, X., Xing, G., Roy, S., and Liu, H. (2019, January 3–6). Experiments with mmwave automotive radar test-bed. Proceedings of the 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA. 10.1109/ieeeconf44664.2019.9048939
[13]
Shimizu, T., Va, V., Bansal, G., and Heath, R.W. (2018, January 6–9). Millimeter wave V2X communications: Use cases and design considerations of beam management. Proceedings of the 2018 Asia-Pacific Microwave Conference (APMC), Kyoto, Japan. 10.23919/apmc.2018.8617303
[14]
Sakaguchi "Towards mmWave V2X in 5G and beyond to support automated driving" IEICE Trans. Commun. (2021) 10.1587/transcom.2020ebi0001
[15]
Nathanson "Radar design principles-Signal processing and the Environment" NASA STI/Recon Tech. Rep. A (1991)
[16]
Skolnik, M.I. (1980). Introduction to Radar Systems, McGraw-Hill.
[17]
Nathanson, F.E. (1990). Radar design principles. Signal Processing and the Environment, McGraw-Hill.
[18]
Ozkaptan "A mmWave MIMO Joint Radar-Communication Testbed With Radar-Assisted Precoding" IEEE Trans. Wirel. Commun. (2024) 10.1109/twc.2023.3337282
[19]
Kumari "Adaptive and fast combined waveform-beamforming design for mmWave automotive joint communication-radar" IEEE J. Sel. Top. Signal Process. (2021) 10.1109/jstsp.2021.3071592
[20]
Thiagarajan, G., Hosur, S., and Gurugopinath, S. (2022, January 11–15). A Multi-Stage Constant False-Alarm Rate Detector for Millimeter Wave Radars. Proceedings of the 2022 IEEE International Conference on Signal Processing and Communications (SPCOM), Bangalore, India. 10.1109/spcom55316.2022.9840827
[21]
Jha, U.S. (2018, January 17–20). The millimeter Wave (mmW) radar characterization, testing, verification challenges and opportunities. Proceedings of the 2018 IEEE AUTOTESTCON, National Harbor, MD, USA. 10.1109/autest.2018.8532561
[22]
Evans, R., Farrell, P., Felic, G., Duong, H.T., Le, H.V., Li, J., Li, M., Moran, W., and Skafidas, E. (2014, January 8–10). Consumer radar: Opportunities and challenges. Proceedings of the 2014 11th European Radar Conference, Rome, Italy. 10.1109/eurad.2014.6991193
[23]
Graff "Deep learning-based link configuration for radar-aided multiuser mmWave vehicle-to-infrastructure communication" IEEE Trans. Veh. Technol. (2023) 10.1109/tvt.2023.3239227
[24]
Gao "Integrated sensing and communication with mmWave massive MIMO: A compressed sampling perspective" IEEE Trans. Wirel. Commun. (2022) 10.1109/twc.2022.3206614
[25]
Ragonese "CMOS automotive radar sensors: Mm-Wave circuit design challenges" IEEE Trans. Circuits Syst. II Express Briefs (2022)
[26]
Prabhakara, A., Jin, T., Das, A., Bhatt, G., Kumari, L., Soltanaghai, E., Bilmes, J., Kumar, S., and Rowe, A. (June, January 29). High resolution point clouds from mmwave radar. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK. 10.1109/icra48891.2023.10161429
[27]
Sim, Y., Heo, J., Jung, Y., Lee, S., and Jung, Y. (2023). FPGA implementation of efficient CFAR algorithm for radar systems. Sensors, 23. 10.3390/s23020954
[28]
Zhou "An Improved Quantile Estimator With Its Application in CFAR Detection" IEEE Geosci. Remote. Sens. Lett. (2023)
[29]
Madjidi, H., Laroussi, T., and Detouche, N. (2023, January 28–29). Percentile-Based Estimator for Generalized Rayleigh CFAR Ship Detection in SAR Imagery. Proceedings of the 2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM), Medea, Algeria. 10.1109/ic2em59347.2023.10419843
[30]
Roldan, I., Palffy, A., Kooij, J.F., Gavrila, D.M., Fioranelli, F., and Yarovoy, A. (2024). See Further Than CFAR: A Data-Driven Radar Detector Trained by Lidar. arXiv. 10.1109/radarconf2458775.2024.10548426
[31]
Li "Integrated Detection and Imaging Algorithm for Radar Sparse Targets via CFAR-ADMM" IEEE Trans. Geosci. Remote Sens. (2023)
[32]
Del Prete, R., Graziano, M.D., and Renga, A. (2025, March 28). Cascade CFAR & SLA Ship Detector for Multi-Frequency SAR Data. Available online: https://www.mdpi.com/2072-4292/15/6/1582. 10.3390/rs15061582
[33]
Shen, W., Zhi, J., Wang, Y., Sun, J., Lin, Y., Li, Y., and Jiang, W. (2023). Two-Step CFAR-Based 3D Point Cloud Extraction Method for Circular Scanning Ground-Based Synthetic Aperture Radar. Appl. Sci., 13. 10.3390/app13127164
[34]
Rosu "Dimension Compressed CFAR for Massive MIMO Radar" IEEE Geosci. Remote. Sens. Lett. (2023) 10.1109/lgrs.2023.3277713
[35]
Liu "Multi-fold high-order cumulants based CFAR detector for radar weak target detection" Digit. Signal Process. (2023) 10.1016/j.dsp.2023.104076
[36]
Liu "A CFAR Detection Algorithm Based on Clutter Knowledge for Cognitive Radar" IEICE Trans. Fundam. Electron. Commun. Comput. Sci. (2023) 10.1587/transfun.2022eap1064
[37]
HM "Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates" Rca Rev. (1968)
[38]
Watts, S. (2000, January 12). The performance of cell-averaging CFAR systems in sea clutter. Proceedings of the Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037], Alexandria, VA, USA.
[39]
Cai "Performance Analysis of Some New CFAR Detectors under Clutter" J. Comput. (2011) 10.4304/jcp.6.6.1278-1285
[40]
Rohling, H. (2011, January 7–9). Ordered statistic CFAR technique-an overview. Proceedings of the 2011 12th International Radar Symposium (IRS), Leipzig, Germany.
[41]
Hofele, F. (2001, January 15–18). An innovative CFAR algorithm. Proceedings of the 2001 CIE International Conference on Radar Proceedings (Cat No. 01TH8559), Beijing, China.
[42]
Liang "A method for threshold setting and false alarm probability evaluation for radar detectors" Signal Process. (2023) 10.1016/j.sigpro.2023.108930
[43]
Cao "The improved constant false alarm rate detector based on multi-frame integration for fluctuating target detection in heavy-tailed clutter" IET Signal Process. (2023) 10.1049/sil2.12145
[44]
Li "Semiparametric constant false alarm rate method for radar and sonar images" Electron. Lett. (2024) 10.1049/ell2.13146
[45]
Wang, Y., Wang, C., Shi, Q., Huang, J., and Yuan, N. (2023). Adaptive optimization technology of segmented reconstruction signal based on genetic algorithm for enhancing radar jamming effect. Front. Phys., 11. 10.3389/fphy.2023.1277361
[46]
Rumney "MIMO over-the-air research, development, and testing" Int. J. Antennas Propag. (2012) 10.1155/2012/467695
[47]
Yang "Fifty years of MIMO detection: The road to large-scale MIMOs" IEEE Commun. Surv. Tutor. (2015) 10.1109/comst.2015.2475242
[48]
Xu "Two decades of MIMO design tradeoffs and reduced-complexity MIMO detection in near-capacity systems" IEEE Access (2017) 10.1109/access.2017.2707182
[49]
El Ayach, O., Heath, R.W., Abu-Surra, S., Rajagopal, S., and Pi, Z. (2012, January 17–20). The capacity optimality of beam steering in large millimeter wave MIMO systems. Proceedings of the 2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cesme, Turkey. 10.1109/spawc.2012.6292865
[50]
Smith "Beam steering with linear arrays" IEEE Trans. Biomed. Eng. (1983) 10.1109/tbme.1983.325149

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Published
Apr 02, 2025
Vol/Issue
14(7)
Pages
1436
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Cite This Article
Boxun Yan, Ian P. Roberts (2025). Advancements in Millimeter-Wave Radar Technologies for Automotive Systems: A Signal Processing Perspective. Electronics, 14(7), 1436. https://doi.org/10.3390/electronics14071436
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