Stepped Frequency Continuous Wave Radar Research Articles

Microwave Structural Health Monitoring of the Grouted Connection of a Monopile-Based Offshore Wind Turbine: Fatigue Testing Using a Scaled Laboratory Demonstrator

Offshore wind turbines play a significant role in the expansion of clean and renewable energy. However, their exposure to harsh marine environments and dynamic loading conditions poses significant challenges to their structural integrity. In particular, the grouted connection, serving as the crucial interface between the monopile and the transition piece, is susceptible to cracking and particle washout that can lead to destabilizing grout erosion over time. In this paper, we propose a microwave structural health monitoring (SHM) approach for damage detection in grouted connections based on a stepped-frequency continuous wave radar. The methodology exploits ultra-wideband (UWB) electromagnetic wave propagation in the frequency range from 100 MHz to 2 GHz, where the microwaves propagate along the concrete-type dielectric material guided by the surrounding steel cylinders. For the proof of concept, a scaled laboratory demonstrator was built that realistically models the dynamic loading experienced by a full-scale monopile. The structure was equipped with an UWB radar system using two transmitting and three receiving antennas directly coupled to the grout. For validation, a large number of other sensors, i.e., accelerometers, strain gauges, and acoustic emission sensors have also been installed and measured synchronously during the fatigue test. It is demonstrated here that the proposed SHM methodology offers a nondestructive and real-time method for assessing the structural integrity of the grouted connection directly, actively, and automatically. This has the potential to support predictive maintenance activities in the future.

Silent speech command word recognition using stepped frequency continuous wave radar

Recovering speech in the absence of the acoustic speech signal itself, i.e., silent speech, holds great potential for restoring or enhancing oral communication in those who lost it. Radar is a relatively unexplored silent speech sensing modality, even though it has the advantage of being fully non-invasive. We therefore built a custom stepped frequency continuous wave radar hardware to measure the changes in the transmission spectra during speech between three antennas, located on both cheeks and the chin with a measurement update rate of 100 Hz. We then recorded a command word corpus of 40 phonetically balanced, two-syllable German words and the German digits zero to nine for two individual speakers and evaluated both the speaker-dependent multi-session and inter-session recognition accuracies on this 50-word corpus using a bidirectional long-short term memory network. We obtained recognition accuracies of 99.17% and 88.87% for the speaker-dependent multi-session and inter-session accuracy, respectively. These results show that the transmission spectra are very well suited to discriminate individual words from one another, even across different sessions, which is one of the key challenges for fully non-invasive silent speech interfaces.

Artifacts Suppression Using Correlation-Weighted Back Projection Imaging Algorithm for Chaotic GPR

A correlation weighted back projection (CWBP) imaging algorithm is proposed to suppress artifacts introduced by the classic back projection (BP) algorithm in the application of chaotic ground-penetrating radar (GPR). In the proposed CWBP algorithm, the backscattering response to each point in the imaging region is first reconstructed through the weighting, which is used to highlight the backscattering response of targets. Then the quadratic cross correlation is calculated to reduce the nontargets’ backscattering. The experimental results show that the artifacts are significantly suppressed. In addition, the ghosts generated by overlapped of multitargets artifacts in the classic BP are removed. Our results also show that the proposed CWBP can be applied for the stepped frequency continuous wave radar.

Continuous Human Activity Recognition through Parallelism LSTM with Multi-Frequency Spectrograms

According to the real-living environment, radar-based human activity recognition (HAR) is dedicated to recognizing and classifying a sequence of activities rather than individual activities, thereby drawing more attention in practical applications of security surveillance, health care and human–computer interactions. This paper proposes a parallelism long short-term memory (LSTM) framework with the input of multi-frequency spectrograms to implement continuous HAR. Specifically, frequency-division short-time Fourier transformation (STFT) is performed on the data stream of continuous activities collected by a stepped-frequency continuous-wave (SFCW) radar, generating spectrograms of multiple frequencies which introduce different scattering properties and frequency resolutions. In the designed parallelism LSTM framework, multiple parallel LSTM sub-networks are trained separately to extract different temporal features from the spectrogram of each frequency and produce corresponding classification probabilities. At the decision level, the probabilities of activity classification from these sub-networks are fused by addition as the recognition output. To validate the proposed method, an experimental data set is collected by using an SFCW radar to monitor 11 participants who continuously perform six activities in sequence with three different transitions and random durations. The validation results demonstrate that the average accuracies of the designed parallelism unidirectional LSTM (Uni-LSTM) and bidirectional LSTM (Bi-LSTM) based on five frequency spectrograms are 85.41% and 96.15%, respectively, outperforming traditional Uni-LSTM and Bi-LSTM networks with only a single-frequency spectrogram by 5.35% and 6.33% at least. Additionally, the recognition accuracy of the parallelism LSTM network reveals an upward trend as the number of multi-frequency spectrograms (namely the number of LSTM subnetworks) increases, and tends to be stable when the number reaches 4.

Efficient multitask learning analyses on grain silo measurement

Determining the amount of grain stored in silos is very important for accurate commercial inventory planning. A convolutional neural network (CNN) is developed for the first time to determine the amount of the grain using step-frequency continuous wave radar (SFCWR) signals. The radar reflection signal of different grain quantity for different grain surface patterns is gathered by means of a constructed experimental setup. 5681 measurements are performed in the scaled model silo containing different weights (0 to 20 kg) grain stacked as different surface patterns. The dataset is then created using the spectrograms of SFCWR signals. While 1420 data randomly selected from the dataset are used for testing, the remaining 4261 data are used for training. The results are then compared with the pretrained CNNs, demonstrating the superiority of the proposed method. The accuracy of the methods is given with metric parameters for both classification and regression. The proposed multitask CNN model obtained higher performance with 0.2865 MAE, 0.5053 MAPE, 0.8047 MSE, and 0.8971 RMSE for regression task and 99.23% accuracy, 99.09% sensitivity, 99.52% specitivity, 99.42% precision, 99.25% F1-score, and 98.83% MCC for classification. These metric performances are better than the previous study with 3.29 MAPE in the literature. The results obtained reveal that, with proper modeling and successful training, CNNs can be effectively used for the quantity measurement applications of the grain stacks.

A Reconfigurable Stepped Frequency Continuous Wave Radar Prototype for Smuggling Contrast, Preliminary Assessment

A Reconfigurable Stepped Frequency Continuous Wave Radar Prototype for Smuggling Contrast, Preliminary Assessment

Analysis of the Snow Water Equivalent at the AEMet-Formigal Field Laboratory (Spanish Pyrenees) During the 2019/2020 Winter Season Using a Stepped-Frequency Continuous Wave Radar (SFCW)

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available the water from the snow cover is the well-known snow water equivalent (SWE). This paper presents a near-surface-based radar for determining the SWE from the measured complex spectral reflectance of the snowpack. The method is based in a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150 MHz to 6 GHz. An electromagnetic model to solve the electromagnetic reflectance of a snowpack, including the frequency and wetness dependence of the complex relative dielectric permittivity of snow layers, is shown. Using the previous model, an approximated method to calculate the SWE is proposed. The results are presented and compared with those provided by a cosmic-ray neutron SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. This experimental field is located in the Spanish Pyrenees at an elevation of 1800 m a.s.l. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow height measurement sensor based on a 120 GHz frequency modulated continuous wave (FMCW) radar sensor, is shown.

A Local Oscillator Phase Compensation Technique for Ultra-Wideband Stepped-Frequency Continuous Wave Radar Based on a Low-Cost Software-Defined Radio.

The paper discusses a way to configure a stepped-frequency continuous wave (SFCW) radar using a low-cost software-defined radio (SDR). The most of high-end SDRs offer multiple transmitter (TX) and receiver (RX) channels, one of which can be used as the reference channel for compensating the initial phases of TX and RX local oscillator (LO) signals. It is same as how commercial vector network analyzers (VNAs) compensate for the LO initial phase. These SDRs can thus acquire phase-coherent in-phase and quadrature (I/Q) data without additional components and an SFCW radar can be easily configured. On the other hand, low-cost SDRs typically have only one transmitter and receiver. Therefore, the LO initial phase has to be compensated and the phases of the received I/Q signals have to be retrieved, preferably without employing an additional receiver and components to retain the system low-cost and simple. The present paper illustrates that the difference between the phases of TX and RX LO signals varies when the LO frequency is changed because of the timing of the commencement of the mixing. The paper then proposes a technique to compensate for the LO initial phases using the internal RF loopback of the transceiver chip and to reconstruct a pulse, which requires two streaming: one for the device under test (DUT) channel and the other for the internal RF loopback channel. The effect of the LO initial phase and the proposed method for the compensation are demonstrated by experiments at a single frequency and sweeping frequency, respectively. The results show that the proposed method can compensate for the LO initial phases and ultra-wideband (UWB) pulses can be reconstructed correctly from the data sampled by a low-cost SDR.

Anechoic chamber measurements for circular isar imaging at Mersin University’s Meatrc Lab

Inverse synthetic aperture radar (ISAR) imaging is a reliable detection and classification technique for maneuvering targets at near and far-field ranges. In this study, we examine the near-field circular (turntable) ISAR imaging by conducting various real measurement experiments that were performed in the microwave anechoic chamber of the Mersin University’s MEATRC laboratory. The backscattered data were collected via a vector network analyzer that works as a Stepped Frequency Continuous Wave (SFCW) radar and for a number of simple and complex metal objects. The collected raw data were calibrated by using the backscattering data of a canonical object and then focused by applying a near-field backprojection image reconstruction algorithm. The resultant circular ISAR images demonstrate successful and well localized detection of various types of targets even though they are camouflaged by clothing. The obtained results reveal the preliminary efficacy of C band ISAR imaging in concealed object detection problem encountered at security checkpoints such as airports.

An experimental study: Detecting the respiration rates of multiple stationary human targets by stepped frequency continuous wave radar

In this study, it is aimed to improve the maximum range and range resolution while detecting multiple targets’ respiration rates. An original algorithm has been proposed for this purpose, and a Stepped Frequency Continuous Wave radar has been set up for experiments. Experiments have been executed with periodically moving plates and human targets. With a resolution of 30 cm, the detected maximum ranges are 7 m and 6.3 m for moving plate and human targets, respectively. In moving plate experiments, the average accuracies of the frequency measurements are above 98% for both single and multiple-target scenarios. In human target experiments, the average accuracy of the respiration rate measurements is 96.58% for single target experiments while it is 94.44% for multiple targets. The results show that the proposed structure outperforms the state-of-the-art benchmark in terms of the capability of sensing the respiration rate in a wide range with a high resolution.

Sparse Nonuniform Frequency Sampling for Fast SFCW Radar Imaging

Stepped frequency continuous wave (SFCW) radar achieves wide bandwidth by synthesizing series of monochromatic pulses in a consecutive manner. Uniform frequency sampling is often performed with a constant frequency step, which in turn limits the maximum unambiguous range achievable by the radar to reliably distinguish targets. Thus, a small frequency step must be selected when clutters exist at long distances even though the target of interests is located at much closer distances. This is costly since a large number of frequencies must be synthesized, which leads to slow acquisition speed. In this paper, a sparse nonuniform frequency sampling method is proposed to effectively reduce the number of frequencies while suppressing aliasing effects from clutters. The Poisson Sum Formula is utilized to derive a deterministic formula for choosing a discrete set of frequencies within a specified frequency band. A corresponding frequency weighting formula is added in order to maintain the same target impulse response in time-domain as the one achieved by dense uniform frequency sampling. Numerical and experimental results are presented to demonstrate the improved performances of the proposed sparse sampling method for SFCW radar imaging.

Stepped-Frequency Continuous-Wave Radar With Self-Injection-Locking Technology for Monitoring Multiple Human Vital Signs

This article proposes a single-conversion stepped-frequency continuous-wave (SCSFCW) radar that combines a stepped-frequency continuous-wave (SFCW) radar and a self-injection-locked (SIL) radar to benefit from the range resolution and the Doppler sensitivity of the two radars. An 8.5–9.5-GHz prototype SCSFCW radar system that comprises a subharmonic up/down converter with a 3–3.5-GHz stepped chirp local-oscillator (LO) signal and a 2.5-GHz SIL IF signal was developed to monitor the vital signs, i.e., respiration rate (RR) and heart rate (HR), of multiple humans. The coherence and range of the developed system were significantly enhanced by using a low pulse repetition frequency (PRF). In the experiment, the minimum distinguishable radial spacing between the vibrating frequencies of the metal plates that are not azimuthally overlapped with one another corresponds to a theoretical range resolution of 15 cm. However, owing to scattering by the human body, the minimum radial spacing for distinguishing between the vital signs of the individuals is three times than that for distinguishing between the metal plates in a similar experimental setup. Accordingly, the monitoring of up to three human vital signs using the developed system was demonstrated with a range-vital-Doppler map.

Machine learning based quantity measurement method for grain silos

In this study, K-nearest neighbour (KNN) algorithm was used to determine grain quantity in the silo via thru the air radar. Thanks to the constituted stepped frequency continuous wave radar (SFCWR) system on a model silo, the back-scattering signal of different amount of grain for different stack structure were obtained. To create the training data, 5680 measurements were performed, and accuracy of the KNN was obtained with k-fold cross validation technique. Range profiles were obtained taking the inverse Fourier transform of SFCWR data and 8 features were extracted from range profiles. To found best feature combination on classification, 255 possible feature combinations were also constituted, then they were trained and tested. The best accuracy was achieved as 96,71% by using selected 5 features. These results show that, if the effective features are extracted from range profiles, the amount of the grain can be successfully determined using machine learning techniques.

Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars

This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to obtain the correlation coefficient matrix. Then, the constant false alarm rate (CFAR) detection is applied to extract the ranges between each target and the two radars, respectively, from the correlation matrix. Finally, the locations of human targets is calculated with the triangulation localization algorithm. This cross-correlation operation mainly brings about two advantages. On the one hand, the cross-correlation explores the correlation feature of target respiratory signals, which can effectively detect all targets with different signal intensities, avoiding the missed detection of weak targets. On the other hand, the pairing of two ranges between each target and two radars is implemented simultaneously with the cross-correlation. Experimental results verify the effectiveness of this algorithm.

Implementation of a simple stepped frequency continuous wave target localization system comprising two antennas based on common region of sensing

This article focuses on the two antenna-based target localization which operates on the principle of stepped frequency continuous wave radars. The detailed study of the system generated limitations for a two antenna-based target localization system toward the practical implementation is considered and consequently, a common region of sensing (CROS) has been identified. The system capability in different sense like target tracking, spatial coverage, and through the wall localization has been investigated properly and it was found that the proposed CROS-based localization scheme is an improved as well as simple method by utilizing small number of antenna, flexibility of antenna placing, less processing with higher accuracy, and the position fixing in a simple Cartesian coordinate with reference to the two antennas.

Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks

Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.

A Doppler Range Compensation for Step-Frequency Continuous-Wave Radar for Detecting Small UAV.

Step-frequency continuous-wave (SFCW) modulation can have a role in the detection of small unmanned aerial vehicles (UAV) at short range (less than 1–2 km). In this paper, the theory of SFCW range detection is reviewed, and a specific method for correcting the possible range shift due to the Doppler effect is devised. The proposed method was tested in a controlled experimental set-up, where a free-falling target (i.e., a corner reflector) was correctly detected by an SFCW radar. This method was finally applied in field for short-range detection of a small UAV.

Algorithm to process the stepped frequency radar signal for a thin road surface application

This article describes an algorithm for calculating the relative permittivity from a stepped frequency continuous wave radar (SFCW) signal. The intended application for the developed radar is a quality estimation of the road surface layer, which includes homogeneity and density estimation. Automated fast computations are needed for road surveying purposes, which must be conducted alongside the normal traffic and with typical traffic speeds. For SFCW, we get N sweep waveforms for each sample point, where N equals the number of frequency steps, which poses a computational problem for onboard data acquisition. Radar signal processing steps are at first conducted in the frequency domain, where initial calibration corrections are applied. This data is then transferred to the time domain via inverse Discrete Fourier Transform. The relative permittivity is calculated from the peak produced by the surface reflection. A proof of the concept is given in the form of a pilot measurement on a real road. Results indicate that the automated signal detection algorithm is accurate enough to be used in real life measurements.

Comparison Study of Noncontact Vital Signs Detection Using a Doppler Stepped-Frequency Continuous-Wave Radar and Camera-Based Imaging Photoplethysmography

In this paper, we compare the performance of radar and optical (camera based) techniques in detecting vital signs such as respiratory rate (RR), heart rate (HR), and blood oxygen saturation (SpO2). Specifically, we investigate the application of ultrawideband stepped-frequency continuous-wave radar and imaging photoplethysmography (iPPG) techniques to measure vital signs. The radar performance can be enhanced by using phase information of backscattered signal instead of its amplitude. On the other hand, the iPPG system can be enhanced by using more than one camera and utilizing very selective narrowband filters coupled with good illumination. In either system, use of advanced signal processing is required to improve accuracy. Generally, HR and RR can be accurately read by either microwave radar or optical techniques with 500 lx illumination level to have < ±2% error up to 2 m distance between the subject and the system, but optical technique errors increase significantly to < ±15% for <200 lx. However, each system has its unique advantages as the radar can be used for seeing-through walls and optical technique is uniquely capable of measuring SpO2).

Simulation framework for compressive sensing‐based through‐wall detection of moving targets

Detection of moving targets in cluttered environments is important in numerous applications, such as disaster search-and-rescue, law enforcement, urban warfare and so on. One of the popular techniques employed for this purpose is the use of stepped-frequency continuous-wave radar due to its low cost and reduced complexity. However, the stepped-frequency radar suffers from long data acquisition time. This study focuses on the detection of moving targets in cluttered medium; such as behind walls, using stepped-frequency radar enhanced by compressive sensing (CS). The application of CS enables the reconstruction of the target space using fewer frequencies, which decreases the acquisition time. This study presents simulation results that demonstrate the applicability of CS to stepped-frequency radar measurements in accurately locating and tracking targets behind walls. The plane-wave spectrum approach is employed in conjunction with through-wall transmission coefficients for calculating the scattered fields of targets behind the wall. The received voltage at the antenna terminals is then computed by adopting the concept of complex antenna factor frequently used in Electromagnetic Compatibility (EMC) community, which fully takes into account the antenna effects, enabling an accurate simulation.