The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions
Abstract
Automotive radar is the most promising and fastest-growing civilian application of radar technology. Vehicular radars provide the key enabling technology for the autonomous driving revolution that will have a dramatic impact on everyone's day-to-day lives. They play a central role in the autonomous sensing suit because of the significant progress in the radio-frequency (RF) CMOS technology that enables high-level radar-on-chip integration and thus reduces the automotive radar cost to the level of consumer mass production. However, this would not be sufficient without high spatial resolution performance, which can be obtained by multiple-input, multiple-output (MIMO) and cognitive approaches at a lower cost. The uniqueness of automotive radar scenarios mandates the formulation and derivation of new signal processing approaches beyond classical military radar concepts. The reformulation of vehicular radar tasks, along with new performance requirements, provides an opportunity to develop innovative signal processing methods. In this article, we first revise conventional techniques for signal processing in automotive radar. Then, we emphasize the limitations of the historically driven conventional processing approaches in practical roadway scenarios and present alternative signal processing solutions. Finally, we propose several future research directions to enhance vehicular radar performance
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The main obstacle for the automotive RADAR system is to yield high-resolution information in an environment that incorporates several dynamic objects in an immensely clustered automotive scene with a high update rate.
“However, the main obstacle for the automotive RADAR system is to yield high-resolution information in an environment that incorporates several dynamic objects in an immensely clustered automotive scene with a high update rate [82,83].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [82]
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- Authors
- Bilik, I., Longman, O., Villeval, S., & Tabrikian, J.
- Affiliations
- Duke University (US)
- Bar-Ilan University (IL)
- Ben-Gurion University of the Negev (IL)
- Venue
- IEEE Signal Processing Magazine
- Published
- 2019-09
- Volume
- 36
- Issue
- 5
- Pages
- 20-31
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2973146207
- Cited by
- 374
- References
- 51
- Citations by year
- 2026: 29 · 2025: 65 · 2024: 67 · 2023: 76 · 2022: 50 · 2021: 56 · 2020: 29 · 2019: 2
- FWCI
- 25.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- automotive radar, autonomous vehicles, radar signal processing, MIMO radar, cognitive radar, spatial resolution, vehicular sensing
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