Frequency-modulated Continuous-wave Radar Sensor Research Articles

Efficient Target Classification Based on Vehicle Volume Estimation in High-Resolution Radar Systems

In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.

Through-Wall Imaging Using Low-Cost Frequency-Modulated Continuous Wave Radar Sensors

Through-Wall Imaging Using Low-Cost Frequency-Modulated Continuous Wave Radar Sensors

Real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision

Gesture-based control has gained prominence as an intuitive and natural means of interaction with unmanned aerial vehicles (UAVs). This paper presents a real-time gesture-based control system for UAVs that leverages the multimodal fusion of Frequency Modulated Continuous Wave (FMCW) radar and vision sensors, aiming to enhance user experience through precise and responsive UAV control via hand gestures. The research focuses on developing an effective fusion framework that combines the complementary advantages of FMCW radar and vision sensors. FMCW radar provides robust range and velocity measurements, while vision sensors capture fine-grained visual information. By integrating data from these modalities, the system achieves a comprehensive understanding of hand gestures, resulting in improved gesture recognition accuracy and robustness. The proposed system comprises three main stages: data acquisition, gesture recognition, and multimodal fusion. In the data acquisition stage, synchronized data streams from FMCW radar and vision sensors are captured. Then, machine learning algorithms are employed in the gesture recognition stage to classify and interpret hand gestures. Finally, the multimodal fusion stage aligns and fuses the data, creating a unified representation that captures the spatial and temporal aspects of hand gestures, enabling real-time control commands for the UAV. Experimental results demonstrate the system‘s effectiveness in accurately recognizing and responding to hand gestures. The multimodal fusion of FMCW radar and vision sensors enables a robust and versatile gesture-based control interface.

Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar

The paper proposes a unsupervised method for detecting the three stages of sleep—wake, rapid eye movement (REM) sleep, and non-REM sleep—using biosignals obtained from a 61 GHz single frequency modulated continuous wave (FMCW) radar. To detect the subject’s sleep stages based on non-learning techniques, the breathing and movement information characteristic of each sleep stage was extracted from the radar signals of the subject acquired in the sleep state and used as the feature factor tailored to the research objective. The experimental results derived from the clinical data obtained in the actual polysomnography (PSG) environment using FMCW radar show an average of 68% similarity to the actual three sleep stages observed in PSG. These results indicate the feasibility of using the FMCW radar sensor as an alternative to the conventional PSG-based method that poses multiple limitations to sleep-stage detection.

Plasma state supervision utilizing millimeter wave radar systems

AbstractThis work introduces a method for plasma state supervision, based on a frequency-modulated continuous wave radar sensor and a suitable signal evaluation enabling a continuous supervision method for the plasma state. Highly precise phase evaluation of the signal allows us to detect and visualize smallest changes in the plasma state. Assuming the plasma to act like a frequency-dependent dielectric material, the propagation of the electromagnetic wave depends on the plasma state and hence, also the measured phase. Broadband measurements are carried out at center frequencies of 80 and 140 GHz in a low-pressure plasma. The radar-based setup can be used for a very flexible application, capable for spatially resolved measurements in the plasma bulk. At the same time, the high measurement rate allows for quasi real-time monitoring, so that transient processes in the plasma are recorded. Due to the simple setup, this approach is most suitable for industrial applications to improve process control. The chosen different frequencies will show a change in the influence of the plasma on the electromagnetic wave demonstrating the advantages of multi-frequency approaches in future applications.

Embedded radar networks for damage detection in wind turbine blades: validation in a full-scale fatigue test

This paper presents the design and experimental realization of a cooperative radar network for structural health monitoring (SHM) of wind turbine blades. For this purpose, 40 FMCW (frequency-modulated continuous wave) radar sensors operating from 58 to 63.5 GHz have been installed in a 31-m-long blade during manufacturing. A subset of 10 sensors is material-embedded in the core material of the blade, and the remaining thirty sensors are placed inside the blade on an inner rotor blade surface. The sensors are distributed over the entire blade based on previous high-frequency electromagnetic simulations. A full-scale fatigue test has been performed under controlled laboratory conditions. In addition, holes have been drilled into the blade by hand to represent a well-defined and relatively small damage. During the experimental campaign, measurements from the complete radar network have been transferred to a base station through a wireless communication link. Finally, it was demonstrated that fatigue as well as artificial damage could be detected accurately using the proposed damage indicator (DI) approach.

A Correction Method for the Nonlinearity of FMCW Radar Sensors Based on Multisynchrosqueezing Transform

The frequency-modulated continuous wave (FMCW) radar sensors have attracted much attention in short-range applications. However, when detecting a stationary target, an FMCW radar sensor obtains the time-varying beat signals due to its nonlinearity, which degrades the imaging quality. For this reason, a correction method for the nonlinearity of FMCW radar sensors is proposed based on the multisynchrosqueezing transform (MSST) in this article. First, a reference beat signal is processed by the short-time Fourier transform (STFT) to generate the blurry time–frequency (TF) representation. Second, an iterative reassignment procedure is employed to concentrate the blurry TF representation by the MSST method. Then, the concentrated TF representation is used to estimate the nonlinear coefficients by ridge detection and polynomial fitting. Finally, the nonlinear beat signals are compensated using the nonlinear coefficients after time resampling. The effectiveness of the proposed method is validated by simulations and experiments. The results show that the proposed method can obtain more concentrated TF representation and more accurate estimation of nonlinear coefficients, resulting in a higher quality of range profiles and synthetic aperture radar (SAR) images. In addition, the proposed method has better noise robustness for the strongly time-varying and multicomponent beat signals.

Wideband Gain Enhancement of MIMO Antenna and Its Application in FMCW Radar Sensor Integrated With CMOS-Based Transceiver Chip for Human Respiratory Monitoring

A novel compact multiple-input multiple-output (MIMO) antenna is proposed with wideband gain enhancement. It consists of two identical antenna elements spacing about half wavelength. First, a 1-to-2 Y-shaped power divider with wideband anti-phase outputs is designed to feed two modified Vivaldi radiators. The currents on the two radiating surfaces keep the same direction in a wideband to increase radiation without being canceled. Second, the square-ring unit cell is analyzed to form metasurface, placed in the front of the radiator. It can guide the forward electromagnetic (EM) radiation while reducing the backward radiation. Third, a U-shaped slot is etched between the two modified Vivaldi radiators. It is approximated as an effective electrically small dipole radiator. Such arrangement further effectively enhances the radiation performance in the direction of the main lobe and destructively interferes with the sidelobe radiation. These features make the antenna far-field radiation pattern reshaped. Thereby, the gain is improved in a wide bandwidth. Meanwhile, the MIMO antenna diversity performance is analyzed with low mutual coupling. Wideband gain enhancement contributes to improve the detection capability of the frequency modulated continuous wave (FMCW) radar sensor. As a proof of concept, the proposed MIMO antenna prototype is fabricated. The measured impedance bandwidth ranges from 11.5 to 21.3 GHz [59.76% fractional bandwidth (FBW)] with isolation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ge 24.92$ </tex-math></inline-formula> dB. The gain is up to 10.6 dBi, the radiation efficiency is 88.01%–90.02%, and the envelope correlation coefficient (ECC) is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\le 0.00122$ </tex-math></inline-formula> . Integrated with the transceiver chip fabricated on the 65 nm CMOS process, the proposed MIMO antenna is applied in the FMCW radar sensor. The test system is built up, and the experiments on different breathing conditions are conducted for different human subjects. The proposed MIMO antenna could reduce measurement error, thereby improving measurement accuracy for human respiratory monitoring.

A Direct Feedback FVF LDO for High Precision FMCW Radar Sensors in 65-nm CMOS Technology.

A direct feedback flipped voltage follower (FVF) LDO for a high-precision frequency-modulated continuous-wave (FMCW) radar is presented. To minimize the effect of the power supply ripple on the FMCW radar sensor's resolution, a folded cascode error amplifier (EA) was connected to the outer loop of the FVF to increase the open-loop gain. The direct feedback structure enhances the PSRR while minimizing the power supply ripple path and not compromising a transient response. The flipped voltage follower with a super source follower forms a fast feedback loop. The stability and parameter variation sensitivity of the multi-loop FVF LDO were analyzed through the state matrix decomposition. We implemented the FVF LDO in TSMC 65 nm CMOS technology. The fabricated FVF LDO supplied a maximum load current of 20 mA with a 1.2 V power supply. The proposed FVF LDO achieved a full-spectrum PSR with a low-frequency PSRR of 66 dB, unity-gain bandwidth of 469 MHz, and 20 ns transient settling time with a load current step from 1 mA to 20 mA.

Ghost Target Suppression Using Deep Neural Network in Radar-Based Indoor Environment Mapping

In this paper, we propose a method to remove ghost targets that occur in radar-based indoor environment mapping. In this work, the ghost target refers to an object that does not physically exist in actual space, but is detected by multiple reflection paths of a radar signal. We first acquire target detection results in an indoor hallway environment using a millimeter-wave band frequency-modulated continuous wave radar sensor. In the detection results, the estimated distance, velocity, and angle values of real and ghost targets have different distributions, which can be used as criteria to classify both types of targets. Therefore, we design a deep neural network (DNN)-based classifier using the target features as input vectors. Through the proposed DNN-based classifier, the ghost targets can be identified and removed from the detection results. We verify the performance of the proposed ghost target suppression method on two different types of radar sensor data sets: 1) radar sensor data acquired from the hallway environment used to train the DNN and 2) radar sensor data acquired from new hallway environments not used for training the DNN. Our proposed method can remove up to 87.36% of ghost targets on average in three different indoor environments.

Low-Loss Heterogeneous Integrations With High Output Power Radar Applications at W-Band

This study presents a design of a 94-GHz high-performance and highly compact frequency-modulated continuous-wave radar sensor. In this sensor, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> -band CMOS-based all-digital phase-locked loop chip, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> -band SiGe-based transceiver monolithic microwave integrated circuit (MMIC), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> -band GaN-based MMIC power amplifier (PA) are heterogeneously integrated (HI). Each part of the 130-nm bipolar complementary metal-oxide-semiconductor (BiCMOS) SiGe-based transceiver MMIC is designed to include a low-noise amplifier, PA, octupler, mixer, and lange coupler. The fabrication process of our in-house silicon-based MEMS photosensitive composite film is developed to provide very high-density integration and very low insertion loss of interconnections between chips. The HI front end of the radar sensor is measured on-wafer with a high output power of 22 dBm. Moreover, a low double-sideband noise figure (NF) of 10.2 dB is obtained. Bonding–substrate integrated waveguide (SIW)–WG transitions between the heterogeneously integrated front end and the antenna are specially designed and verified. A 2.3-dB degradation is observed, where 19.7 dBm is measured at the transmitter WG WR-10-flange. Thus, the sensor offers a 55-dB dynamic range at a 2-m position with an expectation of a long-distance target-detection range. The radar sensor has an 11.7-cm range resolution and is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$60\times 40\times $ </tex-math></inline-formula> 8 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> in size, with a weight of only 78 g.

Near-Field Effects on Micrometer Accurate Ranging With Ultra-Wideband mmWave Radar

We investigated near-field effects on free-space ranging with ultra-wideband millimeter-wave (mmWave) radar. In this application, one often assumes far-field conditions. However, with increasing requirements of accuracy, far-field assumptions are not satisfying. The radar measurements are affected by phase variations, leading to erroneous distance estimates. To simulate and approximate these effects, we propose computational methods by means of physical optics. They were validated using a state-of-the-art frequency-modulated continuous-wave radar sensor. Sophisticated experiments show good agreement; thus, in practice, the methods can be used to correct distance estimates to achieve micron accuracy without extensive calibration in advance.

Solving Phase Ambiguity in Interferometric Displacement Measurement With Millimeter-Wave FMCW Radar Sensors

The frequency-modulated continuous-wave (FMCW) radar sensor is subject to phase ambiguity in measuring large displacement of over half a wavelength, which is particularly severe in the millimeter-wave FMCW radar sensor due to its short wavelength. The state-of-the-art linear phase demodulation techniques were mostly developed for single tone quadrature continuous-wave (CW) radars such as the Modified differentiate and cross-multiply (MDACM) algorithm. This paper presents a novel linear interferometric displacement measurement technique which can synthesize the quadrature <inline-formula> <tex-math notation="LaTeX">${I}/{Q}$ </tex-math></inline-formula> signals across slow time from a single-channel FMCW radar. In this way, the linear phase demodulation technique developed for the CW radar can now be leveraged for the FMCW radar. The theoretical analysis and working principles are discussed. Exhaustive experiments are performed to validate the proposed technique. It is demonstrated that the displacement motions as large as 10 times of the wavelength can be precisely extracted from a single channel FMCW radar with micrometer accuracy and without any phase ambiguity.

A 90 GHz Broadband Balanced 8-Way Power Amplifier for High Precision FMCW Radar Sensors in 65-nm CMOS.

We present a W-band 8-way wideband power amplifier (PA) for a high precision frequency modulated continuous wave (FMCW) radar in 65-nm CMOS technology. To achieve a broadband operation with an improved output power for a high range resolution and high distance coverage of FMCW radar sensors, a balanced architecture is employed with the Lange coupler which naturally combines the output powers from two 4-way push-pull PAs. By utilizing a transformer-based push-pull structure with a cross-coupled capacitive neutralization technique, the gate-drain capacitance of the 4-way PA is compensated for the stabilization with an improved power gain. Interstage matching was performed with transformers for a reduced loss from the matching network and minimal area occupation. The implemented balanced 8-way PA achieved a saturated output power (Psat) of 16.5 dBm, a 1-dB compressed output power (OP1dB) of 13.3 dBm, a power-added efficiency (PAE) of 9.9% at 90 GHz and 3-dB power bandwidth was 20.4 GHz (79.2–99.6 GHz).

High-Speed Continuous Wavelet Transform Processor for Vital Signal Measurement Using Frequency-Modulated Continuous Wave Radar

This paper proposes a high-speed continuous wavelet transform (CWT) processor to analyze vital signals extracted from a frequency-modulated continuous wave (FMCW) radar sensor. The proposed CWT processor consists of a fast Fourier transform (FFT) module, complex multiplier module, and inverse FFT (IFFT) module. For high-throughput processing, the FFT and IFFT modules are designed with the pipeline FFT architecture of radix-2 single-path delay feedback (R2SDF) and mixed-radix multipath delay commutator (MRMDC) architecture, respectively. In addition, the IFFT module and the complex multiplier module perform a four-channel operation to reduce the processing time from repeated operations. Simultaneously, the MRMDC IFFT module minimizes the circuit area by reducing the number of non-trivial multipliers by using a mixed-radix algorithm. In addition, the proposed CWT processor can support variable lengths of 8, 16, 32, 64, 128, 256, 512, and 1024 to analyze various vital signals. The proposed CWT processor was implemented in a field-programmable gate array (FPGA) device and verified through the measurement of heartbeat and respiration from an FMCW radar sensor. Experimental results showed that the proposed CWT processor can reduce the processing time by 48.4-fold and 40.7-fold compared to MATLAB software with Intel i7 CPU. Moreover, it can be confirmed that the proposed CWT processor can reduce the processing time by 73.3% compared to previous FPGA-based implementations.

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.

High-Resolution 400 GHz Submillimeter-Wave Quasi-Optical Radar Imaging System

A submillimeter-wave quasi-optical high-resolution imaging system, based on a frequency-modulated continuous wave radar sensor operating between 343GHz and 423GHz utilizing a bandwidth of 80GHz, is presented. The system setup in-tandem with the essential building blocks is described and the generation of high-resolution power- and elevation-profile images of various samples under test is presented. Experimental results show a lateral resolution limit in the order of wavelength.

Applications of radar measurement technology using 24 GHz, 61 GHz, 80 GHz and 122 GHz FMCW radar sensors

Abstract This paper presents a review of radar applications in high-accuracy distance measurement of a target. The radars included in this review are frequency modulated continuous wave (FMCW) radar sensors operating in four different millimeter-wave frequency bands, namely 24 GHz, 61 GHz, 80 GHz and 122 GHz. The radar sensors are used to measure the distance of standard and complex targets in a short range of a few meters, thus indicating that the choice of target and the medium used for radar signal propagation also play a key role in determining the distance measurement accuracy of an FMCW radar. The standard target is a trihedral corner reflector in a laboratory-based free space measurement setup and the complex targets include a piston in an oil-filled hydraulic cylinder and a planar positioning stage used in micromachining. In each of these measurement scenarios, a distance measurement accuracy in micrometer range is achieved due to the use of a sophisticated signal processing algorithm that is based on a combined frequency and phase estimation method. The paper is concluded with a technical comparison of the accuracy achieved by the FMCW radars reviewed in this article with other related works.

Compressive Sensing-Based SAR Image Reconstruction from Sparse Radar Sensor Data Acquisition in Automotive FMCW Radar System.

In this paper, we propose a method for reconstructing synthetic aperture radar (SAR) images by applying a compressive sensing (CS) technique to sparsely acquired radar sensor data. In general, SAR image reconstruction algorithms require radar sensor data acquired at regular spatial intervals. However, when the speed of the radar-equipped platform is not constant, it is difficult to consistently perform regular data acquisitions. Therefore, we used the CS-based signal recovery method to efficiently reconstruct SAR images even when regular data acquisition was not performed. In the proposed method, we used the l1-norm minimization to overcome the non-uniform data acquisition problem, which replaced the Fourier transform and inverse Fourier transform in the conventional SAR image reconstruction method. In addition, to reduce the phase distortion of the recovered signal, the proposed method was applied to each of the in-phase and quadrature components of the acquired radar sensor data. To evaluate the performance of the proposed method, we conducted experiments using an automotive frequency-modulated continuous wave radar sensor. Then, the quality of the SAR image reconstructed with data acquired at regular intervals was compared with the quality of images reconstructed with data acquired at non-uniform intervals. Using the proposed method, even if only 70% of the regularly acquired radar sensor data was used, a SAR image having a correlation of 0.83 could be reconstructed.

Dual-Mode Radar Sensor for Indoor Environment Mapping.

In this paper, we introduce mapping results in an indoor environment based on our own developed dual-mode radar sensor. Our radar system uses a frequency-modulated continuous wave (FMCW) with a center frequency of 62 GHz and a multiple-input multiple-output antenna system. In addition, the FMCW radar sensor we designed is capable of dual-mode detection, which alternately transmits two waveforms using different bandwidths within one frame. The first waveform is for long-range detection, and the second waveform is for short-range detection. This radar system is mounted on a small robot that moves in indoor environments such as rooms or hallways, and the radar and the robot send and receive necessary information to each other. The radar estimates the distance, velocity, and angle information of targets around the radar-equipped robot. Then, the radar receives information about the robot’s motion from the robot, such as its speed and rotation angle. Finally, by combining the motion information and the detection results, the radar-equipped robot maps the indoor environment while finding its own position. Compared to the actual map data, the radar-based mapping is effectively achieved through the radar system we developed.