Estimation techniques and simulation platforms for 77 GHz FMCW ACC radars

International audienceThis paper presents two radar simulation platforms that have been developed and evaluated. One is based on the Advanced Design System (ADS) and the other on Matlab. Both platforms are modeled using homodyne front-end 77 GHz radar, based on commercially available monolithic microwave integrated circuits (MMIC). Known linear modulation formats such as the frequency modulation continuous wave (FMCW) and three-segment FMCW have been studied, and a new variant, the dual FMCW, is proposed for easier association between beat frequencies, while maintaining an excellent distance estimation of thetargets. In the signal processing domain, new algorithms are proposed for the three-segment FMCW and for the dual FMCW. While both of these algorithms present the choice of either using complex or real data, the former allows faster signal processing, whereas the latter enables a simplified front-end architecture. The estimation performance of the modulation formats has been evaluated using the Cramer-Rao and Barankin bounds. It is found that the dual FMCW modulation format is slightly better than the other two formats tested in this work. A threshold effect is found at a signal-to-noise ratio (SNR) of 12 dB which means that, to be able to detect a target, the SNR should be above this value. In real hardware, the SNR detection limit should be set to about at least 15 dB

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

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.publishedVersio

THE APPLICATION OF LEAKY WAVE ANTENNAS FOR MEDICAL HYPERTHERMIA TREATMENT AND BEAMFORMER IN FMCW AUTOMOTIVE RADAR SYSTEMS

Thousands of years ago human discovered that if a slice of amber is rubbed against fur, it would absorb light-weight objects. Hundreds of years after that the ancient people figured out that there are actually two different characteristics of attraction and repulsion. Another 2000 years passed when human discovered that these two wonders of nature, magnetism and electricity are actually linked together like the two sides of the same coin. Since then, in the early 19th century great huge achievements were made in antennas and propagation by scientists such as Hans Christen Oersted, Heinrich Hertz, Alexander Popov and Marconi. Since then, antennas have found their enormous applications in military, medical and industrial arenas. In the endless world of antennas, we have picked up leaky wave antennas to further investigate their interesting properties and worked on at least 2 applications of such propagation systems in the medical field as well as in automotive field such as road safety. In our research, we have designed one-dimensional and two-dimensional leaky wave antennas to apply the main beam to the cancerous tissue by using the beam scanning property of these antennas. We could shift the main beam to another location in the tissue. Two-dimensional leaky wave antennas provide more beam flexibility in terms of beam geometry and beam displacement which will be discussed in the corresponding chapters. Improving road safety with equipping vehicles with sensing systems such as frequency modulated continuous wave (FMCW) automotive radar systems is one of the most interesting topics in radar engineering. Surely, the main factor that could influence the mass production of cheap radar systems for automobiles is influenced by what antenna system is designed for that radar system. In this dissertation, we have proposed an FMCW radar system using a cheap antenna solution. The proposed antenna system is comb-line leaky wave antenna. The performance of the proposed radar system has been evaluated using range-Doppler graphs and we have also discussed a common problem in multi-target FMCW radar systems which is the issue of the ghost targets

An FPGA-based 77 GHzs RADAR signal processing system for automotive collision avoidance

An FPGA implementable Verilog HDL based signal processing algorithm has been developed to detect the range and velocity of target vehicles using a MEMS based 77 GHz LFMCW long range automotive radar. The algorithm generates a tuning voltage to control a GaAs based VCO to produce a triangular chirp signal, controls the operation of MEMS components, and finally processes the IF signal to determine the range and veolicty of the detected targets. The Verilog HDL code has been developed targeting the Xilinx Virtex-5 SX50T FPGA. The developed algorithm enables the MEMS radar to detect 24 targets in an optimum timespan of 6.42 ms in the range of 0.4 to 200 m with a range resolution of 0.19 m and a maximum range error 0.25 m. A maximum relative velocity of ±300 km/h can be determined with a velocity resolution in HDL of 0.95 m/s and a maximum velocity error of 0.83 m/s with a sweep duration of 1 ms

Localization of multi-class on-road and aerial targets using mmwave FMCW radar

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Concepts for Short Range Millimeter-wave Miniaturized Radar Systems with Built-in Self-Test

This work explores short-range millimeter wave radar systems, with emphasis on miniaturization and overall system cost reduction. The designing and implementation processes, starting from the system level design considerations and characterization of the individual components to final implementation of the proposed architecture are described briefly. Several D-band radar systems are developed and their functionality and performances are demonstrated

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

An Experimental Study of Radar-Centric Transmission for Integrated Sensing and Communications

This study proposes a dual-function radar and communication (DFRC) system that utilizes radar transmission parameters as modulation indexes to transmit data to the users while performing radar sensing as its primary function. The proposed technique exploits index modulation (IM) using the center frequency of radar chirps, their bandwidths, and polarization states as indexes to modulate the communication data within each radar chirp. By utilizing the combination of these indexes, the proposed DFRC system can reach up to 17 Mb/s throughput, while observing a robust radar performance. Through our experimental study, we also reveal the trade-off between the radar sensing performance and communication data rate, depending on the radar waveform parameters selected in the DFRC system. This study also demonstrates the implementation of the proposed DFRC system and presents its real-time over-the-air experimental measurements. Consequently, the simulation results are verified by real-time over-the-air experiments, where ARESTOR, a high-speed signal processing and experimental radar platform, has been employed