© 2017 IEEE. Reprinted, with permission, from IEEE Signal Processing Magazine, March 2017
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 history of radio detection and ranging, more commonly known as radar, starts with the experiments carried out by Hertz and Hülsmeyer on the reflections of electromagnetic (EM) waves and ideas advocated by Tesla and Marconi in the late 19th and early 20th centuries. Earlier developments in radar technology were limited to military applications such as aircraft/ship surveillance, navigation, and weapons guidance. Radar is now used in many applications, including civilian aviation, navigation, mapping, meteorology, radio astronomy, and medicine. The main objectives of a radar system are to detect the presence of one or more targets of interest and estimate their range, angle, and motions relative to the radar .
To the everyday person, tangible applications of radar include speed guns used by law enforcement officers to detect speeding drivers. Action heroes in movies sometimes drive a fancy car with attractive features that can track an enemy’s speed and location, move swiftly and automatically amid obstacles, and debut its night vision feature during the movie’s climax. The ambition of having all of these add-ons to a car has become feasible with the flourishing mm-wave circuit technology and advanced signal processing techniques. Advances in circuit technology reinforced by new signal processing algorithms, machine learning, artificial intelligence, and computervision techniques have made self-driving cars a reality.
Such cars also rely on different sensors such as a laser, a camera, ultrasound, global positioning system, and radar. Among these sensors, radar offers the possibility of seeing long distances ahead of the car in poor visibility conditions, which can help avoid collisions . For example, Google’s self-driving car  has radars mounted on both front and rear bumpers of the vehicle to detect objects in its surroundings.
Automotive radars were first deployed several decades ago. The evolution of automotive radar from its inception to the present has been thoroughly discussed in . With highly integrated and inexpensive mm-wave circuits implemented in silicon, compact automotive radar safety systems have become a popular feature , . Since then, review articles written on automotive radar mostly covered the circuit implementation, market analysis, and architectural-level signal processing   . However, there are many aspects of automotive radar signal processing techniques scattered throughout the literature. For example, a part of the literature may concentrate on detecting the presence or absence of targets, while another might look at radar estimation problems concerning their location and velocity in space relative to the radar , . This article’s goal is to review principal developments in signal processing techniques applied to estimating significant target parameters such as range, velocity, and direction. The article also discusses the characterization of radar waveforms and advanced estimation techniques that enhance the operation of automotive radars. In particular, we review each topic with adequate mathematical framework so as to make this a good start-up document for the newcomer in the field.
Automotive radar classification
Both autonomous and human-driven cars are increasingly using radars to improve drivers’ comfort and safety. For instance, park assist and adaptive cruise control provide comfort, while warning the driver of imminent collisions and overriding control of the vehicle to avoid accidents improve the safety. Figure 1 depicts various such radar subsystems that form ADASs. Each subsystem has unique functionality and specific requirements in terms of radar range and angular measurement capability (Table 1). The next section explains the fundamentals of location and speed estimation using the radar measurements.
Basic automotive radar estimation problems
A radar can simultaneously transmit and receive EM waves in frequency bands ranging from 3 MHz to 300 GHz. It is designed to extract information [i.e., location, range, velocity and radar cross section (RCS)] about targets using the EM waves reflected from those targets. Automotive radar systems typically operate at bands in 24 GHz and 77 GHz portions of the EM spectrum known as mm-wave frequencies so that higher velocity and range resolution can be achieved. Fundamental radar operation involves three main tasks: range (distance), relative velocity, and direction estimation, as discussed next.