MIMO radar technology

Abstract

Automotive radar with a small number of antennas has been used for advanced driver-assistance system (ADAS) purposes since the late 1990s. These early automotive radars mostly provided target detection and velocity information. However, the current generation of automotive radar for ADAS has rather limited ability to resolve closely spaced targets. LiDAR systems have a better angular resolution (less than 1 degree) and have been introduced in Level 4 and Level 5 autonomous driving systems. LiDAR can provide point clouds. Via the use of deep neural networks, such as PointNet [1] and PointNet++ [2], the point clouds can lead to target identification. However, due to its use of light spectrum wavelength, LiDAR is susceptible to bad weather conditions, such as fog, rain, snow, and dust in the air. In addition, the cost of LiDAR is high. On the other hand, automotive radar with millimeter-waveform technology has the potential to provide point clouds at a much lower cost than LiDAR and with more robustness to weather conditions. Such radar is referred to as a "high end radar" or imaging radar [3]. Computer vision techniques [1, 2] that were previously reserved for high-resolution camera sensors and LiDAR systems can be applied to imaging radar data to identify targets. For example, a car can be identified based on two-dimensional (2D) radar points of an imaging radar using PointNet [4]. Imaging radars have been attracting the interest of those developing fully autonomous vehicles, major Tier-1 suppliers, and automotive radar startups.In addition to sensitivity, the important requirements for automotive radar are high resolution, low hardware cost, and small size. Multiple-input multiple-output (MIMO) radar technology has been receiving considerable attention from the automotive radar community because it can achieve high angular resolution with relatively small numbers of antennas and receivers. For that ability, it has been exploited in current generation automotive radar for ADAS as well as in next-generation high-resolution imaging radar for autonomous driving. For autonomous driving, information in both azimuth and elevation is crucial. In particular, the height information of targets is required to enable drive-over and drive-under functions. Two typical scenarios are shown in Figure 3.1. It is safe to drive over a metal beverage can on the road and to drive under a steel pedestrian bridge over the road. To meet such requirement, the array is required to have a large aperture in both azimuth and elevation. The MIMO radar is a good candidate for high-resolution imaging radar for autonomous driving. In the MIMO radar, the targets are first distinguished in range and Doppler domains. Then, large virtual arrays with hundreds of elements can be synthesized to provide high resolution in both azimuth and elevation. As a result, point clouds with similar performance to LiDAR can be generated at a much lower cost.In this chapter, we introduce the concept of imaging radars using MIMO technology, present some examples for synthesizing hundreds of virtual array elements by cascading multiple radar transceivers with each supporting a small number of antennas, and discuss design challenges.