Radar Technology

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Radar Technology

Emadi, Mohammad

Over the last decade, new applications have emerged for radar technology in the automotive industry, such as adaptive cruise control, blind spot detection and automatic emergency braking. It is expected that the continued development of radar will unlock new capabilities for autonomous vehicles and safety systems. Indeed, advancements in integrated circuit design have enabled the development of very high-frequency radars with sophisticated signal processing and machine learning techniques. As a consequence, it is now possible with low power, small form factor and low-cost sensors to have rich point cloud data in dense environments. However, many challenges remain that can reduce precision, recall and accuracy of automotive radar, intra- and inter-platform interference or jamming, multi-radar fusion, multipath effects, false targets and detection ambiguity (range, velocity and angle). Many approaches have been developed to address these challenges; massive hybrid MIMO (Multiple-Input Multiple-Output), compressed sensing methods, sparse arrays, PMCW (Phase-Modulated Continuous Wave) Radar, Artificial Intelligence (AI) algorithms and others. Some of these approaches have already been applied to automotive radars, and some likely will be used in the coming years. This chapter will review why radar is unique, the challenges and the state-of-the-art solutions. We will use intuitive and simplified examples to provide the background needed to understand the utility of radar and these techniques.