Frequency Modulated Continuous Wave Research Articles

Frequency-modulated continuous-wave laser interferometry measurement method based on cross-correlation

Frequency-modulated continuous-wave (FMCW) laser interferometry technology holds significant potential for applications in the fields of ultraprecision manufacturing and high-precision sensing. This paper proposes a novel approach among current phase demodulation methods is based on cross-correlation to address the challenge of this technology. On the basis of nonlinear correction of a distributed feedback laser, the intercepted beat frequency signal was first preprocessed with Z-score signal normalization and a smoothing filter. Subsequently, the interference beat signal was subjected to processing using a correlation method to derive the correlation function. Finally, the phase difference between adjacent beat signals was determined by pinpointing the maximum value of the cross-correlation function, enabling accurate displacement demodulation. Experimental validation was performed by constructing an FMCW laser interferometric displacement measurement system. The results indicated that the standard deviation of the displacement error for the cross-correlation method was 2.41 nm during static measurements. Compared to conventional maximum-point method, the static measurement error of the cross-correlation method has been reduced by 1.43 times. In dynamic measurements in the 500 μm range, The measurement error of the cross-correlation method has been reduced by 6.04 times, avoiding the dynamic measurement positioning problem of conventional feature point demodulation methods and making the measurement results more accurate. This advancement holds substantial practical value in the realm of phase demodulation in laser interferometry.

SiN‐On‐SOI Optical Phased Array LiDAR for Ultra‐Wide Field of View and 4D Sensing

Abstract3D imaging techniques are facilitating autonomous vehicles to build intelligent systems. Optical phased arrays (OPAs) featured by all solid‐state configurations are becoming a promising solution for 3D imaging. However, the majority of state‐of‐art OPAs commonly suffer from severe power degradation at the edge of the field of view (FoV), resulting in limited effective FoV and deteriorating 3D imaging quality. Here, the chained grating antenna and the vernier concept is synergized to design a novel OPA for realizing a record wide 160°‐FoV 3D imaging. By virtue of the chained antenna, the OPA exhibits less than 3‐dB beam power variation within the 160°FoV. In addition, two OPAs with different pitches are integrated monolithically to form a quasi‐coaxial Vernier OPA transceiver. With the aid of a flat beam power profile provided by the chained antennas, the OPA exhibits uniform beam quality at an arbitrary steering angle. The superior beam steering performance enables the OPA to accomplish 160° wide‐FoV 3D imaging based on the frequency‐modulated continuous‐wave (FMCW) LiDAR scheme. The ranging accuracy is 5.5‐mm. Moreover, the OPA is also applied to velocity measurement for 4D sensing. To the best of knowledge, it is the first experimental implementation of a Vernier OPA LiDAR on 3D imaging to achieve a remarkable FoV.

Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

Interference mitigation for FMCW radar via chirp rate estimation and signal separation

As a prevalent system in radar technology, the Frequency modulated continuous wave (FMCW) radar faces significant challenges in interference. This paper proposes a three-step interference mitigation strategy considering the time–frequency (TF) features of interference and time-domain properties of beat signals. Initially, the process involves statistical analysis of chirp rates from high-energy TF points, estimating the matched chirp rates of interference signals. Subsequently, the improved multisynchrosqueezing chirplet transform (IMSSCPT) is suggested for high-resolution TF characterization of interference signals. Utilizing the ridge extraction method, we accurately detect the TF region of interference signals. This precise localization enables the gradual separation of the reconstructed interference from the received signals. Finally, we perform parallel singular value decomposition on Hankel matrices, which are stacked from the mitigated subsequences to obtain the beat subspace and reconstruct the beat signal to reduce the negative effect of noise. A series of experimental comparisons of the proposed algorithm with existing methods have validated its exceptional performance, particularly under conditions of high-level noise.

Silicon-based optical phased array with a reconfigurable aperture for “gaze” scanning of LiDAR

Light detection and ranging (LiDAR) serves as one of the key components in the fields of autonomous driving, surveying mapping, and environment detection. Conventionally, dense points clouds are pursued by LiDAR systems to provide high-definition 3D images. However, the LiDAR is typically used to produce abundant yet redundant data for scanning the homogeneous background of scenes, resulting in power waste and excessive processing time. Hence, it is highly desirable for a LiDAR system to “gaze” at the target of interest by dense scanning and rough sparse scans on the uninteresting areas. Here, we propose a LiDAR structure based on an optical phased array (OPA) with reconfigurable apertures to achieve such a gaze scanning function. By virtue of the cascaded optical switch integrated on the OPA chip, a 64-, 128-, 192-, or 256-channel antenna can be selected discretionarily to construct an aperture with variable size. The corresponding divergence angles for the far-field beam are 0.32°, 0.15°, 0.10°, and 0.08°, respectively. The reconfigurable-aperture OPA enables the LiDAR system to perform rough scans via the large beam spots prior to fine scans of the target by using the tiny beam spots. In this way, the OPA-based LiDAR can perform the “gaze” function and achieve full-range scanning efficiently. The scanning time and power consumption can be reduced by 1/4 while precise details of the target are maintained. Finally, we embed the OPA into a frequency-modulated continuous-wave (FMCW) system to demonstrate the “gaze” function in beam scanning. Experiment results show that the number of precise scanning points can be reduced by 2/3 yet can obtain the reasonable outline of the target. The reconfigurable-aperture OPA (RA-OPA) can be a promising candidate for the applications of rapid recognition, like car navigation and robot vision.

Butler matrix enabled multi-beam optical phased array for two-dimensional beam-steering and ranging

Based on the wavelength transparency of the Butler matrix (BM) beamforming network, we demonstrate a multi-beam optical phased array (MOPA) with an emitting aperture composed of grating couplers at a 1.55 μm pitch for wavelength-assisted two-dimensional beam-steering. The device is capable of simultaneous multi-beam operation in a field of view (FOV) of 60° × 8° in the phased-array scanning axis and the wavelength-tuning scanning axis, respectively. The typical beam divergence is about 4° on both axes. Using multiple linearly chirped lasers, multi-beam frequency-modulated continuous wave (FMCW) ranging is realized with an average ranging error of 4 cm. A C-shaped target is imaged for proof-of-concept 2D scanning and ranging.

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future.

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

Harmonic FMCW Radar System: Passive Tag Detection and Precise Ranging Estimation.

This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4-2.5 GHz frequency range for the forward channel and 4.8-5.0 GHz for the backward channel, this study delves into the various challenges faced during the system's realization. These challenges include selecting appropriate components, calibrating the system, processing signals, and integrating the system components. In addition, we introduce a single-layer passive harmonic tag, developed specifically for assessing the system, and provide an in-depth theoretical analysis and simulation results. Notably, the system is characterized by its low power consumption, making it particularly suitable for short-range applications. The system's efficacy is further validated through experimental evaluations in a real-world indoor environment across multiple tag positions. Our measurements underscore the system's robust ranging accuracy and its ability to mitigate self-interference, showcasing its significant potential for applications in harmonic tag detection and ranging.

Feasibility of the application of frequency modulated continuous wave radar trigger technique in abdominal magnetic resonance imaging

Objective: To evaluate and compare the image quality of T2-weighted abdominal scans using a respiratory belt trigger (RBT) and frequency-modulated continuous wave (FMCW)-trigger (FT) techniques and to explore the feasibility of FT in abdominal magnetic resonance imaging (MRI).Methods: The study prospectively included 28 subjects, each undergoing abdominal scans with both RBT and FT. The analysis focused on 64 inconsistent trigger segments from the respiratory curves triggered by RBT and FMCW. Parameters such as inconsistent trigger type (ITT), number of inconsistent trigger points (ITPs), ratio of ITP (ITR), and single-segment ITR (SITR) were derived from these curves. Image quality was evaluated by two observers using subjective scoring, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The assessments classified image quality as either “good” or “poor.” Consistency in image quality assessment between observers was determined using the kappa test and intraclass correlation coefficient (ICC). The chi-square test, Student’s t-test, and the Mann–Whitney U test were employed to compare the categorical and continuous variables between the RBT and FT groups.Results: The observers showed a high level of agreement in image quality assessment. There were no significant differences in ITR, SITR, acquisition time, SNR, and CNR between the RBT and FT groups (all p > 0.05). Both subjective and objective evaluations indicated no notable difference in image quality between the two groups (p > 0.05).Conclusion: The FMCW trigger technique is a viable alternative to the traditional respiratory belt trigger in scenarios of relatively stable breathing. It offers potential benefits, such as reducing operational demands on technicians and improving MRI workflow efficiency.

Critical Design Considerations on Continuous Frequency Modulation Localization Systems

Real-time locating systems (RTLSs) suffer from clock synchronization inaccuracy among their distributed reference nodes. Conventional systems require periodic time synchronization and typically necessitate a two-way ranging (TWR) clock synchronization protocol to eliminate their measurement errors. Particularly, frequency-modulated continuous-wave (FMCW) time-based location systems pose unique design considerations on the TWR that have a significant impact on the quality of their measurements. In this paper, a valid operation design diagram is proposed for the case of an FMCW time-based TWR synchronization protocol. The proposed diagram represents an intersection area of two boundary curves that indicate the functionality of the system at a given frequency bandwidth, spectral length, and clock synchronization ambiguity. It presents an intuitive illustration of the measurement’s expected accuracy by indicating a larger intersection area for relaxed design conditions and vice versa. Furthermore, the absence of a working condition can easily be detected before proceeding with the actual system development. To demonstrate the feasibility of the proposed diagram, four scenarios with different design constraints were evaluated in a Monte-Carlo model of a basic TWR system. Moreover, an experimental measurement setup demonstrated the validity of the proposed diagram. Both the simulation and experimental outcomes show that the indicated valid conditions and the distribution of the measurements’ accuracy are in very good agreement.

Ultra-broadband multi-tone frequency measurement based on the recirculating frequency shift of a frequency modulated continuous wave

We proposed an ultra-broadband multi-tone frequency measurement (FM) approach based on frequency modulated continuous wave (FMCW). This work aims to achieve wide-range multi-tone FM without image interference, using electrical components with narrow bandwidth and low sampling rate, while maintaining high FM accuracy. The FM range is largely increased by extending the bandwidth of the optical FMCW through a recirculating frequency shift (RFS) loop, from 0.001 GHz-16 GHz to 0.001 GHz-437.5 GHz. The bandwidth-extended optical FMCW coherently beats with a continuous wave (CW) light modulated by the signal under test (SUT) at the balanced photodetector (BPD). The following low-pass filter (LPF) outputs pulses at the time when the frequencies of FMCW and SUT are equal, constructing frequency-to-time mapping (FTTM). Owing to the zero-intermediate-frequency (zero-IF) architecture, image interference is avoided. In addition, the up- and down-chirps of FMCW are used to achieve self-reference, avoiding the utilizing of reference signals, which realizes high FM accuracy. In the experiment, a FM within 0.1 GHz-43.5 GHz is demonstrated using an available microwave generator (MG) with a maximum output frequency of 43.5 GHz. The FM errors are kept within ±10 MHz for all frequencies with a mean and standard deviation of −0.3 MHz and 3.17 MHz, respectively. The multi-tone resolution is about 60 MHz at the FMCW chirp rate of 3.1998 GHz/μs, which is consistent with the theoretical result. According to the theoretical derivation, the multi-tone resolution can be improved to 1 MHz by lowering the FMCW chirp rate.

An FMCW Radar-Based Intelligent System for Non-Contact Detection and Monitoring of Pneumonia Symptoms

Pneumonia is one of the most common contagious respiratory diseases, and one of its symptoms is shortness of breath. This symptom underscores the need for non-contact monitoring methods, which our paper addresses by proposing a strategy that uses Frequency-Modulated Continuous Wave (FMCW) radar to extract breathing waveforms and then classifies them with an eXtreme Gradient Boosting (XGBoost) model. The model performs well on our dataset, using stratified k-fold cross-validation and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction. This intelligent system can correctly identify deep and deep-quick breathing patterns with 98% and 87.5% recall scores, respectively. Integrating FMCW and XGBoost offers a promising solution for early detection and real-time monitoring of pneumonia

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions.

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing-a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems.

Improved accuracy and robustness of electron density profiles from JET's X-mode frequency-modulated continuous-wave reflectometers.

JET's frequency-modulated continuous wave (FMCW) reflectometers have been operating well with the current design since 2005, and density profiles have been automatically calculated intershot since then. However, the calculated profiles had long suffered from several shortcomings: poor agreement with other diagnostics, sometimes inappropriately moving radially by several centimeters, elevated levels of radial jitter, and persistent wriggles (strong unphysical oscillations). In this research, several techniques are applied to the reflectometry data analysis, and the shortcomings are significantly improved. Starting with improving the equilibrium reconstruction that estimates the background magnetic field, adding a ripple correction in the reconstructed magnetic field profile, and adding new inner-wall reflection positions estimated through ray-tracing, these changes not only improve the agreement of reconstructed profiles to other diagnostics but also solve density profile wriggles that were present during band transitions. Other smaller but also persistent wriggles were also suppressed by applying a localized correction to the measured beat frequency where persistent oscillations are present. Finally, the burst analysis method, as introduced by Varela etal. [Nucl. Fusion 46 S693 (2006)], has been implemented to extract the beat frequency from stacked spectrograms. Due to the strong suppression of spurious reflections, the radial jitter that sometimes would span several centimeters has been strongly reduced. The stacking of spectrograms has also been shown to be very useful for stacking recurring events, like small gas puff modulations, and extracting transport coefficients that would otherwise be below the noise level.

A Novel Non-Contact Respiration and Heartbeat Detection Method Using Frequency-Modulated Continuous Wave Radar

A Novel Non-Contact Respiration and Heartbeat Detection Method Using Frequency-Modulated Continuous Wave Radar

Advancing Sustainable Marine Exploration: Highly Efficient Photonic Radar for Underwater Navigation Systems under the Impact of Different Salinity Levels

The exploration of underwater environments for applications like environmental monitoring, scientific research, and surveillance has propelled the significance of underwater wireless navigation. Light waves have emerged as a promising solution, offering the potential to achieve the required data rates and propagation speeds. However, underwater optical wireless navigation faces challenges, particularly limited range. This research investigates a novel FMCW (frequency-modulated continuous wave)-based photonic radar system’s efficacy in detecting underwater vehicles across diverse salinity levels and distances. Numerical simulation evaluations reveal distinct signal-to-noise ratios (SNR) and detected power peaks corresponding to varying salinity levels, demonstrating the system’s sensitivity. At 5 g/L salinity, the detected power peaked at −95 dBm, decreasing to −105 dBm at 15 g/L. SNR analysis indicates robust detection within a 4 m range, with challenges emerging at extended ranges and higher salinity. Despite these challenges, the system shows promise for near-range underwater navigation, contributing to sustainable marine exploration by enhancing the accuracy and efficiency of underwater monitoring systems. This advancement aligns with the goals of sustainable development by supporting the protection of marine ecosystems, promoting scientific understanding of underwater environments, and aiding in the sustainable management of marine resources.

Dynamic analysis of geomaterials using microwave sensing

Precise characterization of geomaterials improves subsurface energy extraction and storage. Understanding geomaterial property, and the complexities between petrophysics and geomechanics, plays a key role in maintaining energy security and the transition to a net zero global carbon economy. Multiple sectors demand accurate and rapid characterization of geomaterial conditions, requiring the extraction of core plugs in the field for full-field characterization and analysis in the laboratory. We present a novel technique for the non-invasive characterization of geomaterials by using Frequency Modulated Continuous Wave (FMCW) radar in the K-band, representing a new application of microwave radar. We collect data through the delivery of FMCW wave interactions with geomaterials under static and dynamic conditions and show that FMCW can detect fluid presence, differentiate fluid type, indicate the presence of metallic inclusions and detect imminent failure in loaded sandstones by up to 15 s, allowing for greater control in loading up to a failure event. Such precursors have the potential to significantly enhance our understanding of, and ability to model, geomaterial dynamics. This low-cost sensing method is easily deployable, provides quicker and more accessible data than many state-of-the-art systems, and new insights into geomaterial behavior under dynamic conditions.

A Novel Machine Learning-Based ANFIS Calibrated RISS/GNSS Integration for Improved Navigation in Urban Environments.

Autonomous vehicles (AVs) require accurate navigation, but the reliability of Global Navigation Satellite Systems (GNSS) can be degraded by signal blockage and multipath interference in urban areas. Therefore, a navigation system that integrates a calibrated Reduced Inertial Sensors System (RISS) with GNSS is proposed. The system employs a machine-learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) as a novel calibration technique to improve the accuracy and reliability of the RISS. The ANFIS-based RISS/GNSS integration provides a more precise navigation solution in such environments. The effectiveness of the proposed integration scheme was validated by conducting tests using real road trajectory and simulated GNSS outages ranging from 50 to 150 s. The results demonstrate a significant improvement in 2D position Root Mean Square Error (RMSE) of 43.8% and 28% compared to the traditional RISS/GNSS and the frequency modulated continuous wave (FMCW) Radar (Rad)/RISS/GNSS integrated navigation systems, respectively. Moreover, an improvement of 47.5% and 23.4% in 2D position maximum errors is achieved compared to the RISS/GNSS and the Rad/RISS/GNSS integrated navigation systems, respectively. These results reveal significant improvements in positioning accuracy, which is essential for safe and efficient navigation. The long-term stability of the proposed system makes it suitable for various navigation applications, particularly those requiring continuous and precise positioning information. The ANFIS-based approach used in the proposed system is extendable to other low-end IMUs, making it an attractive option for a wide range of applications.