For Frequency Modulated Continuous Wave Research Articles

Continuous adiabatic frequency conversion for FMCW-LiDAR

Continuous tuning of the frequency of laser light serves as the fundamental basis for a myriad of applications spanning basic scientific research to industrial settings. These applications encompass endeavors such as the detection of gravitational waves, the development of precise optical clocks, environmental monitoring for health and ecological purposes, as well as distance measurement techniques. However, achieving a broad tuning range exceeding 100 GHz along with sub-microsecond tuning times, inherent linearity in tuning, and coherence lengths beyond 10 m presents significant challenges. Here, we demonstrate that electro-optically driven adiabatic frequency converters utilizing high-Q microresonators fabricated from lithium niobate possess the capability to convert arbitrary voltage signals into frequency chirps with temporal resolutions below 1 µs. The temporal evolution of the frequency correlates accurately with the applied voltage signal. We have achieved to generate 200-ns-long frequency chirps with deviations of less than 1 % from perfect linearity without requiring supplementary measures. The coefficient of determination is R2>0.999\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2>0.999$$\\end{document}. Moreover, the coherence length of the emitted light exceeds 20 m. To validate these findings, we employ the linear frequency sweeps for Frequency-Modulated Continuous Wave (FMCW) LiDAR covering distances ranging from 0.5 to 10 m. Leveraging the demonstrated nanosecond-level tuning capabilities, coupled with the potential to tune the eigenfrequency of lithium-niobate-based resonators by several hundred GHz, our results show that electro-optically driven adiabatic frequency converters can be used in applications that require ultrafast and flexible continuous frequency tuning characterized by inherent linearity and substantial coherence length.

An 80–84.8 GHz PLL with auto-tracking Miller divider for FMCW applications

To generate high frequency signals for frequency modulated continuous wave (FMCW) application, components such as doubler, tripler or multiplier are usually utilized to process further signals from the low frequency voltage controlled oscillator (VCO). In this paper, a phase-locked loop (PLL) is intended to be the primary part used to generate frequency modulated continuous wave (FMCW) signals from 80 to 84.8 GHz by utilizing a fundamental frequency VCO. To divide those high frequency output signal and large output bandwidth, the auto-tracking Miller divider topology is proposed. This new topology can achieve 9 GHz locking range. In order to generate FMCW signals with a straight-line triangular chirp, the cascaded PLL is used. The integrated jitter from 1 kHz to 1 GHz is 887 fs for the cascaded PLL, while the single stage PLL used 1.264 ps. Moreover, when architecture with doubler or multiplier is used, the fundamental tone has an effect towards the next systems, while the cascaded PLL does not. It can be highlighted that this work achieves the best RMS-FMerror/BWchirp and RMS-FMerror/(BWchirp × fc × Tc) with value of 0.013% and 0.77e−12, respectively. The designed PLL for FMCW signal generator is implemented in 28 nm CMOS technology. By using a supply voltage of 1.2 V, the chip consumes power of 102 mW. Including all the chip pads, the implemented circuit occupies a silicon area of 1440 µm × 820 µm.

Integrated nonlinearity calibration optical-electrical engine for FMCW LiDAR application.

We demonstrate an integrated optical-electrical calibration module for improving the nonlinearity of the optical source for frequency-modulated continuous-wave (FMCW) LiDAR applications. The linearity of the light source has a considerable influence on FMCW LiDAR range performance, and calibration is typically necessary. However, a majority of existing calibration techniques are based on separate devices, resulting in high cost and limited integration. Our module is made up of a silicon photonic chip with a long optical delay line, a tunable phase shifter, two balanced photodetectors, and some passive components. For this module, we also built the aided amplification and voltage bias circuits. After packaging this module, we used it with our nonlinearity calibration algorithm to analyze the laser's relative nonlinearity. After nonlinearity calibration, the laser relative nonlinearity 1-r2 could be improved to 10-6∼10-7. In the future, the calibration result of nonlinearity could be enhanced further by increasing the length of the on-chip optical delay line.

A fully differential wideband analog front end for FMCW LiDAR application

To increase the system spatial resolution, this paper presents an eight-channel analog front-end (AFE) with wide bandwidth, low noise for frequency modulation continuous wave (FMCW) LiDAR application. The eight channels are independent of each other. For each channel, the fully differential shunt feedback transimpedance amplifier is adopted to improve the signal noise ratio, and the adaptive dc current cancellation (ADCC) is proposed to prevent the saturation with dc current. The cascaded differential amplifier is employed to achieve gain boosting, bandwidth compensation, and better driving capability. The proposed AFE is fabricated in 0.18 μm CMOS technology. Experimental results indicate the largest transimpedance gain, bandwidth, input referred noise current and dc cancellation are 100 dB, 190 MHz, 37.7 nArms and 280 μA, respectively. The power consumption of each channel is 70 mW with a 3.3 V power supply. The total area of the proposed AFE is approximately equal to 1.2 mm × 2.4 mm, including IO pads.

Distance recovery via swept frequency mixing for data-efficient FMCW LiDAR.

We propose a novel, to the best of our knowledge, distance recovery method via swept frequency mixing for frequency-modulated continuous wave (FMCW) light detection and ranging (LiDAR) to overcome the Nyquist limit and obtain high data efficiency. A one dimensional (1D) experiment was conducted to recover the optical fiber length; in addition, a 3D image was obtained by recovering the distances of several targets in free space. Compared to conventional methods based on fast Fourier transform (FFT), beat frequency up to 14 times the Nyquist limit for sampling frequency was successfully measured without aliasing. The proposed method dramatically increases the data efficiency in FMCW LiDAR by reducing the number of complex algorithms and experimental resources required.

Channel Measurement and Ray-Tracing Simulation for 77 GHz Automotive Radar

Millimeter-wave automotive radar is essential for realizing autonomous driving. Realistic channel model and simulation are important for system and sensing algorithm design. This work introduces channel measurements and ray-tracing (RT) channel simulations for frequency modulated continuous wave (FMCW) automotive mmWave radar. The 77 GHz channel measurements in an urban crossroads environment are presented. The dominant echoes (multi-path components) of the measurement are detected and matched with corresponding objects for each frame. A measurement-based electromagnetic (EM) parameter estimation method is proposed to find the optimal parameter set that minimizes the error of simulated radar cross section (RCS). As a result, the issue of lacking reliable EM material parameters for RT simulation at 77 GHz frequency band is tackled. Simulation results are validated in power, range, velocity, and angle domains with the provided EM parameter set. Extending validated RT simulations in similar environments with various configurations allows more reliable channels for rigorous testing without the limitation of channel measurement.

Precise chirp control with model-based reinforcement learning for broadband frequency-swept laser of LiDAR.

Artificial intelligence (AI) has been widely used in various fields of physics and engineering in recent decades. In this work, we introduce model-based reinforcement learning (MBRL), which is an important branch of machine learning in the AI domain, to the broadband frequency-swept laser control for frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR). With the concern of the direct interaction between the optical system and the MBRL agent, we establish the frequency measurement system model on the basis of the experimental data and the nonlinearity property of the system. In light of the difficulty of this challenging high-dimensional control task, we propose a twin critic network on the basis of the Actor-Critic structure to better learn the complex dynamic characteristics of the frequency-swept process. Furthermore, the proposed MBRL structure would stabilize the optimization process greatly. In the training process of the neural network, we apply a delaying strategy to the policy update and introduce a smoothing regularization strategy to the target policy to further enhance the network stability. With the well-trained control policy, the agent generates the excellent and regularly updated modulation signals to control the laser chirp precisely and an excellent detection resolution is obtained eventually. Our proposed work demonstrates that the integration of data-driven reinforcement learning (RL) and optical system control gives an opportunity to reduce the system complexity and accelerate the investigation and optimization of control systems.

Human Activity Recognition Method Based on FMCW Radar Sensor with Multi-Domain Feature Attention Fusion Network.

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9-5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.

Narrow Linewidth External Cavity Laser Capable of High Repetition Frequency Tuning for FMCW LiDAR

A light source with well-rounded performance including the narrow linewidth, wide frequency tuning range, high repetition rate and good linearity, is highly sought-after for frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR). In this work, we report a directly frequency modulated external cavity laser with the linewidth of 5.06 kHz. The frequency tuning range of 1.1 GHz, 445.5 MHz, 228.9 MHz are demonstrated at the repetition rate of 1 kHz, 10 kHz and 100 kHz, respectively. The frequency sweep linearization is achieved by iterative learning pre-distortion. The ranging performance with the repetition rate up to 100 kHz is investigated. The signal-to-noise ratio is still higher than 35 dB while the length of delay fiber is 156 m, corresponding to a target 110 m away in free space. These results indicate that the proposed swept-frequency laser has great potential for autonomous driving applications.

Bandwidth-Enhanced Circularly Polarized mm-Wave Antenna With On-Chip Ground Plane

This article presents a design methodology and a realization of an antenna radiating circular polarization (CP) for Frequency Modulated Continuous Wave (FMCW) radar at mm-wave frequencies on-chip, which combines antennas of different resonance frequencies to increase the operation bandwidth and allow for high-resolution radar. The antenna consists of four dipoles with an on-chip ground plane operating at two resonance frequencies combined with a matching and feeding network, which enables both frequency selectivity in advantage for the resonant antenna and radiation of circular polarization. The dipole arms are based on shorted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\lambda }{/4}$ </tex-math></inline-formula> resonators, which are enhanced with series capacitances for increased radiation efficiency. A method for the broadband characterization of CP antennas and the measurement results of the designed CP antenna are presented. It is shown that the antenna covers a bandwidth between 220 and 260 GHz, indicating the feasibility of both the measurement method and the antenna concept.

ML-HGR-Net: A Meta-Learning Network for FMCW Radar Based Hand Gesture Recognition

In recent years, radar-based hand gesture recognition (HGR) has attracted much attention in the field of human-computer interaction (HCI) due to its benefits of high recognition accuracy and independence from lighting conditions. Conventional deep learning (DL) based models for HGR rely on a large amount of labelled data for training to achieve a satisfactory recognition accuracy. However, it is usually time-consuming and labor-intensive for collecting large amounts of radar data and also difficult to cover all possible hand gesture classes. In this paper, we introduce meta-learning to address the few-shot learning problem for frequency modulated continuous wave (FMCW) radar based HGR. We propose a meta-learning network to learn a model that can quickly adapt to unseen hand gesture tasks with few training observations. In particular, we propose a 3D convolutional neural network (CNN) based dual-channel fusion network for feature extraction by exploiting the correlations of multiple features in radar echo signals to improve the recognition accuracy. In addition, we also develop a learnable relation module with neural networks as a non-linear classifier to measure the similarity between the samples of different hand gestures, which can be more applicable for the HGR task. Finally, we evaluate the performance of the proposed model by conducting experiments on an FMCW radar hardware system. Experimental results show that the proposed meta-learning model substantially enhances the recognition accuracy compared with the state-of-the-art methods including DL and meta-learning based models for FMCW radar-based HGR.

W‐Band 76–81 GHz Millimeter‐Wave Comb‐Line Array for Automotive Short Range Radar

AbstractIn this paper, we design a W‐band millimeter‐wave antenna for the band of 76–81 GHz for frequency modulated continuous wave (FMCW) multiple input multiple output radar systems in automotive applications. It exhibits radiation patterns that possess narrow beamwidths with minimal beam squint in the elevation plane within the band for detection of vehicles and pedestrians in the forward direction instead of objects on the pavement and above the horizon, as well as a wide beamwidth in the horizontal plane, all as required by automotive radar antennas. A four‐port array of comb‐line antennas with diagonally tilted stubs is herein designed, the latter feature for mitigation of interruption from oncoming vehicles traveling along the opposite direction. In order to suppress the mutual interference between multiple antennas in the array, an electromagnetic band gap (EBG) structure is designed to enhance the isolation between ports. A prototype of the design was manufactured and tested, yielding results that satisfy the design requirements.

Low-Power Switching Scheme with Quarter Reference Voltage Sources for SAR ADCs

In this paper, an energy-efficient switching scheme with additional quarter-reference voltage sources in a successive approximation register (SAR) analog-to-digital converter (ADC) is proposed for a low power and small area device for frequency modulated continuous wave (FMCW) radar transceivers. Recently, state-of-the-art ADCs have adopted a configuration that also uses Vref/2 as the reference voltage of the ADCs to improve the switching energy of capacitive digital-to-analog converter (CDAC). The proposed switching configuration additionally uses Vref/4 and 3Vref/4 reference voltages as the reference voltage of CDAC. Compared to state-of-the-art configurations that use the additional reference voltage of Vref/2, the average switching energy, and the total capacitance of CDAC in the proposed configuration are reduced by about 87.5% and 50%, respectively. In this switching scheme, the CDAC output voltage gradually converges to Vref/2, like with conventional SAR ADCs, which minimizes the dynamic offset that deteriorates the linearity of the SAR ADC.

Directly modulated optical negative feedback lasers for long-range FMCW LiDAR.

Frequency sweep operation of directly modulated optical negative feedback lasers is numerically and experimentally investigated for frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) with a high signal-to-noise ratio (SNR), particularly over a long range. Low FM noise corresponding to a spectral linewidth of ∼2.0 kHz is sustained even with injection current modulation of an optical negative feedback laser through optical feedback from a Fabry-Perot etalon, and a beat note spectrum with a 30-dB SNR is achieved even when a 300-m delay fiber is used as a ranging sample. These results encourage an approach to provide directly modulated frequency-swept lasers for long-range FMCW LiDAR.

An efficient target detection algorithm via Karhunen‐Loève transform for frequency modulated continuous wave (FMCW) radar applications

This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high-performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen - Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input-dependent signals orthogonal to each other under new eigen-basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal-to-noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection (Pd).

An 8.55–17.11-GHz DDS FMCW Chirp Synthesizer PLL Based on Double-Edge Zero-Crossing Sampling PD With 51.7-fsrms Jitter and Fast Frequency Hopping

This article proposes a phase-locked loop (PLL) based on the direct digital synthesis (DDS)/digital-to-analog converter (DAC) and the double-edge zero-crossing sampling phase detector (DS-PD) for frequency-modulated continuous-wave (FMCW) radar. Its DS-PD and frequency divider are combined to achieve refined time resolution while effectively expanding the phase lock range, thus eliminating the frequency-locked loop (FLL). The DDS/DAC is used to generate FMCW signals while achieving fast frequency hopping. A differential eight switching current unit is proposed to implement an ultrahigh-speed time-interleaved DAC. The 8.55–17.11-GHz PLL prototype, fabricated in 65-nm CMOS, consumes 10.11 mW with 0.29-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> active area, while the DDS/DAC consumes 12.0 mW with a 0.16-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> active area. The measured in-band phase noise (PN) at a 17.11-GHz output is −120.2 dBc/Hz at a 1-MHz offset with a root-mean-square (rms) jitter of 51.7 fs. The reference spur is <−51 dBc. Finally, a figure-of-merit (FoM) value of −255.7 dB is obtained. The prototype PLL effectively generates fast (500 MHz/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.6~\mu \text{s}$ </tex-math></inline-formula> ) and precise (105-kHzrms frequency error) triangular chirps for FMCW radar applications.

FPGA-Based EO-PLL With Repetitive Control for Highly Linear Laser Frequency Tuning in FMCW LIDAR Applications

We developed a digital electro-optical phase-locked-loop (EO-PLL) utilizing a repetitive control, allowing the use of very nonlinear tunable lasers for frequency-modulated continuous-wave (FMCW) light detection and ranging (LIDAR) applications. This approach allows reducing the phase error of continuously repeated frequency chirps to virtually zero by using information of previous chirps and additionally guaranties sub-second settling times. With this method, much higher nonlinearities in the laser frequency modulation slope <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</i> are tolerable than with common real-time analog PLLs. Furthermore, we applied a Goertzel algorithm generalized to non-integer multiples of the fundamental frequency to achieve a mean measurement precision of 5.41 µm and 16.35 µm at a distance from the interferometer baseline of 3 m and 10.3 m, respectively.

Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.

Prior-Guided Deep Interference Mitigation for FMCW Radars

In this paper, the interference mitigation problem is tackled as a regression problem. A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. Considering the complex-valued nature of radar signals, complex-valued convolutional neural network, which is different from the conventional real-valued counterparts, is utilized as an architecture for implementation. Meanwhile, as the desired beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared with the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. The average SINR of interfered signals has been improved from -9.13 dB to 10.46 dB. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.

Compensation of Sensor Movements in Short-Range FMCW Synthetic Aperture Radar Algorithms

Radar imaging using the synthetic aperture radar (SAR) principle is a common method to obtain information about e.g., the surface of a target. However, most image formation algorithms for such systems assume (quasi-)static measurements. This may lead to errors in the processed images if the sensor is moving during the measurement process. This is especially the case for frequency-modulated continuous-wave (FMCW)-based sensors since the signal duration is longer than in a pulsed system and the achievable bandwidth is much larger and introduces additional challenges. Motion compensation in the context of radar imaging is usually related to the correction of deviations from an ideal trajectory. In contrast, this article presents a method to take the sensor movement during a single FMCW ramp into account and therefore addresses the effects caused by a continuous path during the transmit/receive process. Hence, faster movement can be achieved during the scanning of the synthetic aperture without being bound by stop-and-go approximations. In addition, it will be shown that the algorithm is suitable to reduce systematic errors due to aliasing caused by spatial sampling below the Nyquist rate. For this purpose, this article presents simulations and measurement results, obtained by an ultrawideband <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> -band FMCW radar operating between 122 and 170 GHz.