IEEE Signal Processing Magazine· 2017Q1· Review
Automotive radars: A review of signal processing techniques
- 1,105citations
- Q1SCImago
- 2017year
Short summary
This review consolidates diverse signal processing techniques for automotive radars, crucial for object detection, positioning, and speed estimation in self-driving cars and ADAS.
AI-generated from the title and abstract; the full text is not read.
Key points
- Automotive radars are key sensors for object detection, positioning, and speed estimation in autonomous vehicles and ADAS.
- Signal processing techniques, alongside mm-wave semiconductor technology, are vital for automotive radar performance.
- The review covers waveform design, radar architectures, estimation algorithms, and implementation trade-offs.
- Specific challenges such as pedestrian detection are addressed.
- The article synthesizes scattered literature to guide new researchers and inform the community.
AI-generated from the title and abstract; the full text is not read.
Abstract
Automotive radars, along with other sensors such as lidar, (which stands for "light detection and ranging"), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter-wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird's-eye view to the existing research community.
The authors' abstract, as published at the source. IEEE Signal Processing Magazine, 2017 · DOI ↗
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Field: Aerospace Engineering
Aerospace EngineeringEngineering