Frequency Modulated Continuous Wave Research Articles

High range resolution spectral-scanning LiDAR based on optical frequency-domain reflectometry.

Spectral scanning, which utilizes the dispersive effect of light, is a simple and robust method for solid-state beam steering in light detection and ranging (LiDAR) applications. Powered by a tunable laser source, optical frequency-domain reflectometry (OFDR) is a high-precision measurement scheme that is inherently compatible with spectral scanning. Here, we propose a spectral-scanning LiDAR based on OFDR technology and demonstrate that, by connecting the measured spectral reflectivity and group delay of the targets with the dispersion equation, their cloud point data can be obtained. Moreover, compared to the spectral-scanning LiDAR based on the frequency-modulated continuous-wave (FMCW) ranging method, our proposed LiDAR scheme offers a more than tenfold improvement in range resolution with a large number of angular pixels. This enhancement enables high-resolution 3D imaging along both the angular and range axes.

RF sensing enabled tracking of human facial expressions using machine learning algorithms.

Automatic analysis of facial expressions has emerged as a prominent research area in the past decade. Facial expressions serve as crucial indicators for understanding human behavior, enabling the identification and assessment of positive and negative emotions. Moreover, facial expressions provide insights into various aspects of mental activities, social connections, and physiological information. Currently, most facial expression detection systems rely on cameras and wearable devices. However, these methods have drawbacks, including privacy concerns, issues with poor lighting and line of sight blockage, difficulties in training with longer video sequences, computational complexities, and disruptions to daily routines. To address these challenges, this study proposes a novel and privacy-preserving human behavior recognition system that utilizes Frequency Modulated Continuous Wave (FMCW) radar combined with Machine Learning (ML) techniques for classifying facial expressions. Specifically, the study focuses on five common facial expressions: Happy, Sad, Fear, Surprise, and Neutral. The recorded data is obtained in the form of a Micro-Doppler signal, and state-of-the-art ML models such as Super Learner, Linear Discriminant Analysis, Random Forest, K-Nearest Neighbor, Long Short-Term Memory, and Logistic Regression are employed to extract relevant features. These extracted features from the radar data are then fed into ML models for classification. The results show a highly promising classification accuracy of 91%. The future applications of the proposed work will lead to advancements in technology, healthcare, security, and communication, thereby improving overall human well-being and societal functioning.

Distance and Angle Insensitive Radar-Based Multi-Human Posture Recognition Using Deep Learning

Human posture recognition has a wide range of applicability in the detective and preventive healthcare industry. Recognizing posture through frequency-modulated continuous wave (FMCW) radar poses a significant challenge as the human subject is static. Unlike existing radar-based studies, this study proposes a novel framework to extract the postures of two humans in close proximity using FMCW radar point cloud. With radar extracted range, velocity, and angle information, point clouds in the Cartesian domain are retrieved. Afterwards, unsupervised clustering is implemented to segregate the two humans, and finally a deep learning model named DenseNet is applied to classify the postures of both human subjects. Using four base postures (namely, standing, sitting on chair, sitting on floor, and lying down), ten posture combinations for two human scenarios are classified with an average accuracy of 96%. Additionally, using the centroid information of human clusters, an approach to detect and classify overlapping human participants is also introduced. Experiments with five posture combinations of two overlapping humans yielded an accuracy of above 96%. The proposed framework has the potential to offer a privacy-preserving preventive healthcare sensing platform for an elderly couple living alone.

Neural networks empowered: a machine learning-enabled, Gyro mmID for enhanced virtual reality and motion tracking applications

Abstract With the emerging developments in millimeter-wave/5G technologies, the potential for wireless Internet of things devices to achieve widespread sensing, precise localization, and high data-rate communication systems becomes increasingly viable. The surge in interest surrounding virtual reality (VR) and augmented reality (AR) technologies is attributed to the vast array of applications they enable, ranging from surgical training to motion capture and daily interactions in VR spaces. To further elevate the user experience, and real-time and accurate orientation detection of the user, the authors proposes the utilization of a frequency-modulated continuous-wave (FMCW) radar system coupled with an ultra-low-power, sticker-like millimeter-wave identification (mmID). The mmID features four backscattering elements, multiplexed in amplitude, frequency, and spatial domains. This design utilizes the training of a supervised learning classification convolutional neural network, enabling accurate real-time three-axis orientation detection of the user. The proposed orientation detection system exhibits exceptional performance, achieving a noteworthy accuracy of 90.58% over three axes at a distance of 8 m. This high accuracy underscores the precision of the orientation detection system, particularly tailored for medium-range VR/AR applications. The integration of the FMCW-based mmID system with machine learning proves to be a promising advancement, contributing to the seamless and immersive interaction within virtual and augmented environments.

Signal Processing for Novel Noise Radar Based on de-chirp and Delay Matching

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments.

Acoustic Signal Reconstruction Across Water–Air Interface Through Millimeter-Wave Radar Micro-Vibration Detection

Water surface micro-amplitude waves (WSMWs) of identical frequency are elicited as acoustic waves propagating through water. This displacement can be translated into an intermediate frequency (IF) phase shift through transmitting a frequency modulated continuous wave (FMCW) towards the water surface by a millimeter-wave radar, and information transmission across the water–air interface is achieved via the signal reconstruction method. In this paper, a novel mathematical model based on energy conversion from underwater acoustic to vibration (ECUAV) is presented. This method was able to obtain WSMW vibration information directly by measuring the sound source level (SL). An acoustic electromagnetic wave-based information transmission (AEIT) system was integrated within the water tank environment. The measured distribution of SL within the frequency range of 100 Hz to 300 Hz exhibited the same amplitude variation trend as predicted by the ECUAV model. Thus, the WSMW formation process at 135 Hz was simulated, and the phase information was extracted. The initial vibration information was retrieved through a combination of phase unwinding and Butterworth digital filtering. Fourier transform was applied to the vibrational data to accurately reproduce the acoustic frequency of underwater nodes. Finally, the dual-band binary frequency shift keying (BFSK) modulated underwater encoding acoustic signal was effectively recognized and reconstructed by the AEIT system.

Integration of fixed-frequency and FM-CW (frequency-modulated continuous-wave) reflectometers for coincident turbulence measurements on LTX-β (Lithium Tokamak eXperiment-β).

The fixed-frequency and frequency-modulated continuous-wave (FM-CW) reflectometers on LTX-β (Lithium Tokamak eXperiment-β) have been configured to use the same transmission lines and antenna arrays for coincident views of the core and edge plasma. The fixed-frequency channels (13.1-20.5 and 20-40GHz, tunable between discharges) provide time-resolved measurements of density fluctuations, while the FM-CW channels (13.1-20.2 and 19.5-33.5GHz) measure the density profile and fluctuations, with high spatial resolution and a sampling rate determined by the frequency sweep interval (5 μs). Data from both reflectometers are synchronously acquired to simultaneously leverage the wide bandwidth and high spatial resolution of the respective systems. Experiments showed that mutual crosstalk interference is momentary and does not diminish the capability of either system. Spectral analysis indicated broad power spectra (several hundreds of kHz) and suggests that the signals from the FM-CW system are consistent with under-sampled fixed-frequency signals. Radial correlations were explored using data from the two reflectometers, as well as from the FM-CW system alone. The core channels showed high levels of agreement between these two comparisons, suggesting that the data from the reflectometers are interchangeable for statistical estimates. For the edge channels, comparisons using data from the FM-CW reflectometer alone showed significant decorrelation due to time lag caused by the finite frequency up-sweep duration. Alternatively, this effect is eliminated when cross-correlating data from the different reflectometers. These results highlight the advantages of operating the fixed-frequency and FM-CW reflectometers in this manner, where the combined system can overcome the limitations of each separate system.

Dispersion response broadband tunable underwater FMCW blue chirped laser source

Frequency-modulated continuous-wave (FMCW) narrow linewidth lasers have served as the cornerstone behind applications such as autonomous driving, wearable technology, virtual reality, and remote sensing mapping. Strongly coherent lasers are typically used for these studies, with a clear demand for linear fast response and wide frequency tuning range. In this paper, profiting from the ultrahigh-quality factor of the crystalline whispering-gallery-mode resonator, by using a self-injection locking mechanism to suppress spontaneous emission noise and improve coherence, sub-kHz linewidth at 450 nm is obtained. Furthermore, based on the dispersive response principle, fast electrical tuning is realized by using the strain-influenced resonator, and the experimental test result reaches 81 pm/V. More importantly, we demonstrate the comprehensive performance of this type of FMCW laser in underwater detection, with a sensitivity of 319 MHz/m at a chirp frequency of 1 kHz.

Phase coded waveforms for integrated sensing and communication systems

AbstractNowadays, especially with the developments in the automotive industry, it has become a highly popular topic for radar and communication systems to operate on the same platform and perform joint tasks to increase situational awareness. Sharing the hardware of the radar and communication systems and using a joint waveform for both functions eliminate interference problems between the systems, increasing the effectiveness of the joint task. In studies where radar signals are utilised for communication functions, continuous wave frequency modulation or intra‐pulse frequency modulation is generally employed to transmit communication signals. There are a few studies in the literature on joint radar and communication waveforms using phase modulation. In these studies, phase modulation is not employed directly for both functions. As a contribution to the literature, this study evaluates the usage of joint radar and communications waveforms, such as Barker sequences and Zadoff–Chu sequences as opposed to linear frequency modulation. We compare these waveforms terms of range‐speed detection and symbol error rate.

Noncontace Monitoring of Respiration and Heartbeat Based on Two-Wave Model Using a Millimeter-Wave MIMO FM-CW Radar

This paper deals with the non-contact measurement of heartbeat and respiration using a millimeter-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FM-CW) radar. Monitoring heartbeat and respiration is useful for detecting cardiac diseases and understanding stress levels. Contact sensors are not suitable for these sorts of long-term measurements due to the discomfort and skin irritation they cause. Therefore, the use of non-contact sensors, such as radars, is desirable. In this study, we obtained heartbeat and respiration information from phase data measured using a millimeter-wave MIMO FM-CW radar. We propose a two-wave model based on a Fourier series expansion and extract respiration and heartbeat information as a minimization problem. This model makes it possible to produce respiration and heartbeat waveforms. The produced heartbeat waveform can be used for estimating the interbeat interval (IBI). Experiments were conducted to confirm the usefulness of the proposed method. Moreover, the estimated results were compared with the contact sensor’s results. The results for both types of sensors were in good agreement.

Frequency-Modulated Continuous-Wave Chirp Frequency Error and Phase Noise Measurement: How to Characterize Frequency-Modulated Continuous-Wave Chirp Using Commercial Oscilloscope

Frequency-Modulated Continuous-Wave Chirp Frequency Error and Phase Noise Measurement: How to Characterize Frequency-Modulated Continuous-Wave Chirp Using Commercial Oscilloscope

Detection of Fast-Moving and Accelerating Target Using Linear Frequency-Modulated Continuous-Wave Radars

Detection of Fast-Moving and Accelerating Target Using Linear Frequency-Modulated Continuous-Wave Radars

Ku-Band SAR-Drone System and Methodology for Repeat-Pass Interferometry

In recent years, drone-based Synthetic Aperture Radar (SAR) systems have emerged as flexible and cost-efficient solutions for detecting changes in the Earth’s surface, retrieving topographic data, or detecting ground displacement processes in localized areas, among other applications. These systems offer a unique combination of short and versatile revisit times and flexible acquisition geometries that are not achievable with space-borne, airborne, or ground-based SAR sensors. However, due to platform limitations and flight stability issues, they also present significant challenges regarding instrument design and data processing, particularly when generating interferometric repeat-pass datasets. This paper demonstrates the feasibility of repeat-pass interferometry using a Ku-band drone-based SAR system. The system integrates a dual-channel Ku-band Frequency Modulated Continuous Wave (FMCW) radar with cross-track single-pass interferometric capabilities, mounted on a drone platform. The proposed repeat-pass interferometric processing chain leverages an accurate Digital Elevation Model (DEM), generated from the single-pass interferograms, to precisely coregister the entire stack of acquisitions, thereby producing repeat-pass interferograms free from residual motion errors. The results underscore the potential of this system and the processing chain proposed for generating multi-temporal repeat-pass stacks suitable for repeat-pass applications.

Design and Optimization of a Fan-Out Wafer-Level Packaging- Based Integrated Passive Device Structure for FMCW Radar Applications

This paper presents an integrated passive device (IPD) structure based on fan-out wafer-level packaging (FOWLP) for the front end of frequency-modulated continuous wave (FMCW) radar systems, focusing on enhancing the integration efficiency and performance of large passive components like antennas. Additionally, a new metric is introduced to assess this structure’s effect on the average noise figure in FMCW systems. Using this metric as a loss function, we apply the support vector machine (SVM) for electromagnetic simulation and the genetic algorithm (GA) for optimization. The sample fitting variance is 2.42 dB, reducing computation time from 12 min to under 1 millisecond, with the entire optimization completed in less than 100 s. The optimized IPD structure is 0.7 × 0.9 × 0.014 λ03 in size and achieves over 35 dB isolation between the transmitter and receiver. Compared to the IPD model calculated by empirical formulas, the optimized device lowers the average noise figure by 15.2 dB and increases maximum gain by 4.19 dB.

Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building

This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort.

Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar.

Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar's coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation.

Enhanced Vital Parameter Estimation Using Short-Range Radars with Advanced Motion Compensation and Super-Resolution Techniques

Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RRs and CRs). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RRs and CRs even in the presence of body movement.

Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure.

Signal Extension Method for Improved Range Resolution of Frequency-Modulated Continuous Wave Radar in Indoor Environments

Signal Extension Method for Improved Range Resolution of Frequency-Modulated Continuous Wave Radar in Indoor Environments

Wideband FMCW radar for long-distance target detection based on photonic chirp pulse stitching.

Current wideband frequency-modulated continuous-wave (FMCW) radar fails in detecting long-distance target, since only the target whose echo is temporally close to the reference linear frequency modulation (LFM) chirp can be detected. We propose a long-distance target detection method for the wideband FMCW radar based on photonic chirp pulse stitching. The stitching photonic chirp pulse that is generated through optical up- and downconversion of the LFM is employed as local signal for the de-chirping process instead of the LFM itself, as currently used. By adjusting the transmitting signal period, a theoretically arbitrarily large detection distance can be obtained with the proposed method. Experiments obtain high-resolution range profiles for two targets located at 1.44 km and 14.5 km, respectively, proving the feasibility of the proposed scheme.