Microwave Doppler Radar Research Articles

Model-based estimation of heart movements using microwave Doppler radar sensor.

Heart rate is one of the most crucial vital signs and can be measured remotely using microwave Doppler radar. As the distance between the body and the Doppler radar sensor increases, the output signal weakens, making it difficult to extract heartbeat waveforms. In this study, we propose a new template-matching method that addresses this issue by simulating Doppler radar signals. This method extracts the heartbeat waveform with higher accuracy while the participant is naturally sitting in a chair. An extended triangular wave model was created as a mathematical representation of cardiac physiology, taking into account heart movements. The Doppler radar output signal was then simulated based on this model to automatically obtain a template for one cycle. The validity of the proposed method was confirmed by calculating the PPIs using the template and comparing their accuracy to the R-R intervals (RRIs) of the electrocardiogram for five participants and by analyzing the signals of eight participants in their natural state using the mathematical model of heart movements. All measurements were conducted from a distance of 500 mm. The correlation coefficients between the RRIs of the electrocardiogram and the PPIs using the proposed method were examined for five participants. The correlation coefficients were 0.93 without breathing and 0.70 with breathing. This demonstrates a higher correlation considering the long distance of 500 mm, and the fact that body movements were not specifically restricted, suggesting that the proposed method can successfully estimate RRI. The average correlation coefficients, calculated between the Doppler output signals and the templates for each of the eight participants, exceeded 0.95. Overall, the proposed method showed higher correlation coefficients than those reported in previous studies, indicating that our method performed well in extracting heartbeat waveforms. Our results indicate that the proposed method of remote heart monitoring using microwave Doppler radar demonstrates higher accuracy in estimating the RRI of the electrocardiogram while at rest sitting in a chair, and the ability to extract the heartbeat waveforms from the measured Doppler output signal, eliminating the need to create templates in advance as required by conventional template matching methods. This approach offers more flexibility in the measurement environment than conventional methods.

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

Through the wall human heart beat detection using single channel CW radar.

Single-channel continuous wave (CW) radar is widely used and has gained popularity due to its simple architecture despite its inability to measure the range and angular location of the target. Its popularity arises in the industry due to the simplicity of the required components, the low demands on the sampling rate, and their low costs. Through-the-wall life signs detection using microwave Doppler Radar is an active area of research and investigation. Most of the work in the literature focused on utilizing multi-channel frequency modulated continuous wave (FMCW), CW, and ultra-wideband (UWB) radar for their capability of range and direction of arrival (DOA) estimation. In this paper, through-the-wall single-subject and two-subject concurrent heart rate detection using single-channel 24-GHz CW radar leveraged with maximal overlap discrete wavelet transform (MODWT) is proposed. Experimental results demonstrated that the repetitive measurement of seven different subjects at a distance of 20cm up to 100cm through two different barriers (wood and brick wall) showed an average accuracy of heart rate extraction of 95.27% for varied distances (20-100cm) in comparison with the Biopac ECG acquisition signal. Additionally, the MODWT method can also isolate the independent heartbeat waveforms from the two subjects' concurrent measurements through the wall. This involved four trials with eight different subjects, achieving an accuracy of 97.04% for a fixed distance of 40cm from the Radar without estimating the angular location of the subjects. Notably, it also superseded the performance of the direct FFT method for the single subject after 40cm distance measurements. The proposed simpler architecture of single-channel CW radar leveraged with MODWT has several potential applications, including post-disaster search and rescue scenarios for finding the trapped, injured people under the debris, emergency evacuation, security, surveillance, and patient vital signs monitoring.

Microwave Doppler Radar Occupancy Sensing for HVAC Energy Savings

Microwave Doppler Radar Occupancy Sensing for HVAC Energy Savings

Heart Rate Variability-Based Obstructive Sleep Apnea Events Classification Using Microwave Doppler Radar

Heart Rate Variability-Based Obstructive Sleep Apnea Events Classification Using Microwave Doppler Radar

River Discharge Inversion Algorithm Based on the Surface Velocity of Microwave Doppler Radar

Non-contact methods, which are of great significance to the measurement of river discharge, can not only improve the efficiency of measurement but can also ensure the safety of equipment and personnel. However, owing to their inherent drawbacks such as the requirement of riverbed topography measurements and the difficulty in determining hydrological parameters such as equivalent roughness height, velocity index, etc., there are still challenges for measuring river discharge with high levels of efficiency and accuracy using non-contact methods. To overcome the aforementioned challenges, a new river discharge inversion method is proposed in this paper. In this method, vertical velocities are divided into inner and outer region velocities which can be described by the logarithmic law and the parabolic law, respectively. Applying the river surface velocities collected by microwave Doppler radar and the vertical velocity distributions, the water depths are estimated according to the continuity of the vertical velocities and the shear stresses, and then, the river discharges are obtained by the velocity–area method. The proposed method not only has a simple formula but also comprehensively considers the influence of different hydrological conditions, making it suitable for different river widths and water depths. In this paper, surface velocities collected by microwave Doppler radar on the Yangtze River and the San Joaquin River are used to invert the river discharge, and the results show that for wide–shallow, wide–deep, and narrow–shallow river conditions, the mean percent error (MPE) values of the discharges invertedby the proposed method are 3.91%, 3.82%, and 3.6%, respectively; the root mean square error (RMSE) values are 4.53%, 5.19%, and 4.81%, respectively; and the maximum percent error (MaPE) is less than 15%. The results prove that the proposed method can invert the river discharge with high efficiency and high accuracy under different river widths and water depths without measuring water depth in advance, making it is possible to automatically measure the river discharge in real time.

Study of the food-searching activity by smell in diving beetles of Cybister Curtis, 1827 and Hydaticus Leach, 1817 (Coleoptera: Dytiscidae) including the use of a microwave Doppler radar

To investigate how four species of Cybister Curtis, 1827 and one species of Hydaticus Leach, 1817 (Coleoptera: Dytiscidae) detect food, insects were provided with dried sardines using traps made by a three-dimensional printer. More than 83% of the beetles found the sardine hidden in the trap. When the two traps were set in an aquarium—one contained a dried sardine before extraction with boiled water, and another contained a dried sardine after extraction—more than 90% of beetles were caught in the trap with the sardine before extraction. In contrast, less than 3% of beetles entered the trap with the sardine after extraction. No feeding preference was found between the sardines before and after extraction. A microwave Doppler radar was used to quantitatively detect the behaviour using broth extracted from the sardines, and the broth enhanced the food-searching activity. These results suggest that these beetles use smell to find food, and their searching activities are induced by smell.

Building occupancy estimation using microwave Doppler radar and wavelet transform

Room-level occupancy estimation is a critical input for real estate management, including building utilization optimization, energy-efficient building control, building security, and occupant health and well-being. Knowing how many people are using a room at a time can enable businesses to use their real estate, energy and human capital more efficiently, reducing operating expenses as well as carbon footprint. Radar-based occupancy estimation is attractive because it is unobtrusive and does not introduce the privacy issues brought with video imaging-based sensors. In this paper, we present an occupant estimation approach based on continuous wave Doppler radar and wavelet-based signal processing techniques. Theoretical background provides a rational for using a wavelet-based time-frequency approach, and comprehensive simulation and experimental results demonstrate the effectiveness of the proposed approaches on a data set that includes 1–10 occupants. These results indicate that Doppler radar with wavelet-based signal processing may be an effective tool for occupant count in smart building applications.

Identity Authentication in Two-Subject Environments Using Microwave Doppler Radar and Machine Learning Classifiers

Identity authentication based on Doppler radar respiration sensing is gaining attention as it requires neither contact nor line of sight and does not give rise to privacy concerns associated with video imaging. Prior research demonstrating the recognition of individuals has been limited to isolated single-subject scenarios. When two equidistant subjects are present, identification is more challenging due to the interference of respiration motion patterns in the reflected radar signal. In this research, respiratory signature separation techniques are functionally combined with machine learning (ML) classifiers for reliable subject identity authentication. An improved version of the dynamic segmentation algorithm (peak search and triangulation) was proposed, which can extract distinguishable airflow profile-related features (exhale area, inhale area, inhale/exhale speed, and breathing depth) for medium-scale experiments of 20 different participants to examine the feasibility of extraction of an individual’s respiratory features from a combined mixture of motions for subjects. Independent component analysis with the joint approximation of diagonalization of eigenmatrices (ICA-JADE) algorithm was employed to isolate individual respiratory signatures from combined mixtures of breathing patterns. The extracted hyperfeature sets were then evaluated by integrating two different popular ML classifiers, k-nearest neighbor (KNN) and support vector machine (SVM), for subject authentication. Accuracies of 97.5% for two-subject experiments and 98.33% for single-subject experiments were achieved, which supersedes the performance of prior reported methods. The proposed identity authentication approach has several potential applications, including security/surveillance, the Internet-of-Things (IoT) applications, virtual reality, and health monitoring.

Intrusion Detection System using Machine Learning and Microwave Doppler Radar

A security system is an essential part of a safe home as thefts and intrusions are increasing in recent times; moreover, keeping the home safe is a major concern while being away. This paper proposes an innovative way of security alert system using a microwave doppler radar and integrating it with other peripherals such as camera, alarm, and raspberry pi, implemented using machine learning techniques. The machine learning technique used is the deep neural networks. This structure helps in monitoring the target space while receiving notifications on WhatsApp and Email to improve the security.

Sleep Posture Recognition With a Dual-Frequency Microwave Doppler Radar and Machine Learning Classifiers

An automated, robust, noncontact sleep posture recognition technique is proposed in this letter, which uses optimizable (Bayesian hyperparameter tuning) machine learning (ML) classifiers applied to dual-frequency (2.4 GHz, 5.8 GHz) monostatic continuous-wave radar-measured effective radar cross section and chest displacement. The technique is demonstrated to accurately recognize three different key sleep postures categories for 20 participants, with greater accuracy and computational efficiency than prior published research involving either a custom ML model or threshold-based assessment. Three ML classifiers (K-nearest neighbor, support vector machine (SVM), and decision tree) were assessed, with an SVM using a quadratic kernel achieving an accuracy of 85 and 80%, at 2.4 and 5.8 GHz, respectively, and the decision tree classifier recognizing sleep postures in less than 2 min with 98.4% accuracy for dual-frequency combined measurements.

Velocity Distribution Inversion Method Based on the RANS Equations Using Microwave Doppler Radar

Microwave Doppler radar has been used to make non-contact river flow measurements and offers a high resolution and a wide range. However, discharge estimation with microwave Doppler radar is challenging due to the inability to measure flow velocity below the river surface based on the Doppler shift. To address this problem, a velocity distribution inversion method based on the Reynolds-Averaged Navier-Stokes (RANS) equations is proposed. A classical two-region model which divides the flow into the inner region and the outer region is applied. Velocity at the inner-outer boundary is estimated using surface velocity and the log law is used to estimate velocity distribution of the inner region. Taking velocities at the surface and the inner-outer boundary as boundary conditions, the velocity distribution in the outer region is derived by solving the RANS equations. Discharge is calculated by combining the proposed method with the velocity-area method. The results illustrated that the velocity distribution obtained by the proposed method and measured by an acoustic instrument are in reasonable agreement. Discharge estimated using the obtained velocity distribution has an error less than 10%. The work indicates the potential of microwave Doppler radar to retrieve the river cross section velocity distribution and estimate discharge.

An observational study of sleep characteristics in elite endurance athletes during an altitude training camp at 1800 m

ObjectivesTo observe changes in sleep from baseline and during an altitude training camp in elite endurance athletes. DesignProspective, observational. SettingBaseline monitoring at <500 m for 2 weeks and altitude monitoring at 1800 m for 17-22 days. ParticipantsThirty-three senior national-team endurance athletes (mean age 25.8 ± S.D. 2.8 years, 16 women). MeasurementsDaily measurements of sleep (using a microwave Doppler radar at baseline and altitude), oxygen saturation (SpO2), training load and subjective recovery (at altitude). ResultsAt altitude vs. baseline, sleep duration (P = .036) and light sleep (P < .001) decreased, while deep sleep (P < .001) and respiration rate (P = .020) increased. During the first altitude week vs. baseline, deep sleep increased (P = .001). During the first vs. the second and third altitude weeks, time in bed (P = .005), sleep duration (P = .001), and light sleep (P < .001) decreased. Generally, increased SpO2 was associated with increased deep sleep while increased training load was associated with increased respiration rate. ConclusionThis is the first study to document changes in sleep from near-sea-level baseline and during a training camp at 1800 m in elite endurance athletes. Ascending to altitude reduced total sleep time and light sleep, while deep sleep and respiration rate increased. SpO2 and training load at altitude were associated with these responses. This research informs our understanding of the changes in sleep occurring in elite endurance athletes attending training camps at competition altitudes.

Acquisition and Enhancement of Remote Human Vocal Signals Based on Doppler Radar

In order to achieve remote voice acquisition, a 9.75-GHz microwave Doppler radar with a high-sensitivity coherent homodyne demodulator is used to detect voice signals. However, the combined noise sources, including background noise and shock noise, are included in the Doppler audio radar-captured voice, which seriously damages the quality of speech. In the current work, a multi-stage enhancement method is used to enhance Doppler audio radar-captured voice. The first stage is dictionary learning, which uses the K-singular value decomposition algorithm to train speech and noise dictionaries. The noise training data come from the Doppler audio radar-captured shock noise signals under the influence of different relative motion modulations. The second stage generates a position mask of the shock noise. The third stage is the removal of background noise and shock noise. The removal of background noise adopts our proposed constrained low-rank sparse matrix decomposition algorithm based on power spectral density. The final speech spectrum with shock noise removed is obtained through sparse restoration using the LARC algorithm. The experimental results show that the 9.75-GHz Doppler audio radar can effectively acquire human voice signals, and the proposed multi-stage enhancement method can effectively remove the background noise and shock noise in the radar speech, thereby improving the quality of the audio radar voice. The obtained results imply the potential to implement practical Doppler audio radar systems for human voice signal acquisition.

A Review on Low-Cost Microwave Doppler Radar Systems for Structural Health Monitoring.

Portable, low-cost, microwave radars have attracted researchers’ attention for being an alternative noncontact solution for structural condition monitoring. In addition, by leveraging their capability of providing the target velocity information, the radar-based remote monitoring of complex rotating structures can also be accomplished. Modern radar systems are compact, able to be easily integrated in sensor networks, and can deliver high accuracy measurements. This paper reviews the recent technical advances in low-cost Doppler radar systems for phase-demodulated displacement measurements and time-Doppler analysis for structural health information, including digital signal processing and emerging applications related to radar sensor networks.

Granular Fertilizer Mass Flow Measurement and Vehicle Experiments Based on Microwave Doppler Method

HighlightsA microwave Doppler-based fertilizer mass flow measurement system was developed.The measurement system was applied in vehicle experiments.Power spectra of mass flow signal and vibration signal were obtained and analyzed.Interference suppression algorithm based on CA-CFAR reduced vibration interference.Fertilizer mass flow measurement accuracy was improved by interference suppression.Abstract. The fertilizer mass flow measurement system is typically installed on the fertilizer applicator. However, vehicle vibrations are inevitable during field operation of fertilizing equipment, and can interfere with mass flow signals, thereby affecting the accuracy of mass flow measurements. In this article, a mass flow measurement system based on the microwave Doppler method was introduced. The dominant frequency (fdot) related to fertilizer velocity and the power spectral density (PSD) related to fertilizer concentration were obtained from the Doppler signal of the granular fertilizer processed using fast Fourier transform. The product of fdot and PSD is defined as the sensor output value (SOV). The relationship between SOV, PSD, and fertilizer mass flow (FMF) was studied under vehicle conditions. The linear regression models of 22-8-10 (MOP) high-nitrogen poly-?-glutamic acid fertilizer were established using the least squares method, and the vibration signals were measured. Based on analysis of the power spectra of the mass flow and vibration signals, an interference suppression algorithm based on cell average constant false alarm rate (CA-CFAR) was presented to reduce vibration interference. To verify the performance of the novel vibration interference reduction algorithm, vehicle experiments were conducted at different tractor velocities. The results show that mass flow measurements ranged from 1300 to 3000 g min-1. It was found that using only the PSD related to concentration for flow measurement gave better measurement accuracy compared with SOV, and relative errors of the system were within 8.1%. Correlation between the PSD and FMF improved with interference suppression algorithm based on CA-CFAR. The determination coefficient increased from 0.01 to 0.92, 0.97 to 0.99, and 0.96 to 0.98 at three different tractor velocities, respectively. From the results, it was evident that the algorithm effectively eliminated vibration interference signals in vehicle conditions. In future work, the conditions of the soil will be considered and the performance of the mass flow measurement system will be tested in the field. Keywords: Fertilizer mass flow, Microwave Doppler radar, Tractor vibration, Vehicle experiment, Vibration interference suppression.

Comparing traffic intensity estimates employing passive acoustic Radar and microwave Doppler Radar sensor

The purpose of our applied research project is to develop an autonomous road sign with built-in radar devices of our design. In this paper, we show that it is possible to calibrate the acoustic vector sensor in such a way that it can be used to measure traffic volume and to count the vehicles involved in the traffic through the analysis of the noise emitted by them. Signals obtained from a Doppler radar are used as a reference source. Although the acoustical vector sensor (AVS), being the embodiment of acoustic radar, has a lower accuracy than Doppler radar in vehicle counting and it is not able to measure the vehicle speed with the same precision, it has some advantages over the Doppler sensor. Namely, it does not emit any signals, it is not susceptible to electromagnetic interferences and it allows for further analysis of audio signals, such as assessment of the road surface state (e.g., wet/dry). The acoustic radar we developed is a new proposition of the acoustic method for road traffic monitoring. In addition, our research allowed a comparison of the efficiency of both methods, i.e., microwave and acoustic ones. [Project No. POIR.04.01.04-0089/16 entitled: “INZNAK – Intelligent road signs with V2X interface for adaptive traffic controlling” is financed by the Polish National Centre for Research and Development from under the EU Operational Programme Innovative Economy.]

The Delineation of Fiducial Points for Non-Contact Radar Seismocardiogram Signals Without Concurrent ECG.

Non-contact sensing of seismocardiogram (SCG) signals through a microwave Doppler radar is promising for biomedical applications. However, the delineation of fiducial points for radar SCG still relies on concurrent ECG which requires a contact sensor and limits the complete non-contact detection of SCG. Instead of ECG, a new reference signal, the radar displacement signal of heartbeat (RDH), was derived through the complex Fourier transform and the band pass filtering of the radar signal. The RDH signal was used to locate each cardiac cycle and mask the systolic profile, which was further used to detect an important fiducial point, aortic valve opening (AO). The beat-to-beat interval was estimated from AO-AO interval and compared with the gold standard, ECG R-to-R interval. For the 22 subjects in the study, the evaluation of the AOs detected by RDH (AORDH) shows the average detection ratio can reach 90%, indicating a high ratio of the AORDH that are exactly the same as AO detected using the ECG R-wave (AOECG). Additionally, the left ventricular ejection time (LVET) values estimated from the ensemble averaged radar waveform through AORDH segmentation are within 2 ms of those through AOECG segmentation, for all the detected subjects. Further analysis demonstrates that the beat-to-beat intervals calculated from AORDH have an average root-mean-square-deviation (RMSD) of 53.73 ms when compared with ECG R-to-R intervals, and have an average RMSD of 23.47 ms after removing the beats in which AO cannot be identified. Radar signal RDH can be used as a reference signal to delineate fiducial points for non-contact radar SCG signals. This study can be applied to develop complete non-contact sensing of SCG and monitoring of vital signs, where contact-based SCG is not feasible.

Radar-Based Non-Contact Continuous Identity Authentication

Non-contact vital signs monitoring using microwave Doppler radar has shown great promise in healthcare applications. Recently, this unobtrusive form of physiological sensing has also been gaining attention for its potential for continuous identity authentication, which can reduce the vulnerability of traditional one-pass validation authentication systems. Physiological Doppler radar is an attractive approach for continuous identity authentication as it requires neither contact nor line-of-sight and does not give rise to privacy concerns associated with video imaging. This paper presents a review of recent advances in radar-based identity authentication systems. It includes an evaluation of the applicability of different research efforts in authentication using respiratory patterns and heart-based dynamics. It also identifies aspects of future research required to address remaining challenges in applying unobtrusive respiration-based or heart-based identity authentication to practical systems. With the advancement of machine learning and artificial intelligence, radar-based continuous authentication can grow to serve a wide range of valuable functions in society.

Wireless Sleep Apnea Detection Using Continuous Wave Quadrature Doppler Radar

A non-contact and non-invasive physiological radar monitoring system (PRMS) is introduced. The system is based on motion detection using quadrature microwave Doppler radar, eliminating the need to place sensors on the body. An algorithm was designed to perform real-time actigraphy and sleep apnea detection. The system was validated with clinical polysomnography (PSG) in a sleep study facility on ten consented volunteers with known obstructive sleep apnea. Data obtained from both PRMS and clinical PSG systems were rated by a sleep technician and show an excellent agreement in the detected apnea and hypopnea events. The apnea-hypopnea events were distinguished with an overall sensitivity of 86%, the specificity of 91% and accuracy of 92%.