Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Abstract

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.

Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars

“…It is crucial to note that, at the time this article is being published, the dynamic nature of technological advancements may bring new sensors on the horizon. A review of recent advances in mmWave radar sensors offered a comparison of complete radar systems and single-chipset transceivers in a common table [8]. This served as a good repository for a comparison of the specifications but offered little insight apropos the efficacy for a specific use case.…”

“…There has been a plethora of work carried out in the domain of machine learning/deep learning (ML/DL) using radar sensors for automotive applications. Surveys in [8] and [68,69] show the potential research directions and experimental research conducted. ML/DL are traditionally employed in object detection and classification within ADASs.…”

“…This breakthrough has opened massive opportunities in terms of highly flexible waveform design leading to a high performance in a miniature and cost-effective package. Initially designed for advanced driver assistance systems (ADASs) and limited only to high-end luxury cars, they now find widespread applications beyond the automotive domain [8]. Given the cost-effectiveness and wide availability, it is intrinsically appealing for application engineers and academicians to devise novel methods and techniques to stretch performance beyond hardware constraints.…”

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

“…From a different perspective, mmWave radar sensors provide accurate target range and velocity information, surpassing the other alternatives under extreme weather conditions [293]. One example is IWR6843, which provides a range of detection of 180 m and an ±120 elevation field of view [294]. At the final destination, and although LiDAR mainly aims at generating 3D maps of the environment (via analysis of the reflect light from emitted laser pulses), it can as well be utilized for UAV positioning (e.g., providing precise measurements of a UAV's altitude above ground level [295]).…”

Wide-Angle Beam Steering Closed-Form Pillbox Antenna Fed by Substrate-Integrated Waveguide Horn for On-the-Move Satellite Communications

“…While the traditional parabolic antenna is a high-performance solution, its large size makes it unsuitable for most of those applications. Therefore, there is a growing need for flat, compact, lightweight, and mechanically robust antennas that align with the specific requirements of these systems [7][8][9][10][11]. Additionally, the antennas' gain and beam coverage range play a significant role in overcoming path loss in high-frequency bands and enhancing resolution [12][13][14][15][16][17][18][19].…”