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In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems. [ABSTRACT FROM AUTHOR]

Ultra-wideband radar technology (UWB) has demonstrated its vital role through various applications in surveillance, search and rescue, health monitoring, and the military. Unlike conventional radars, UWB radars use high-frequency, wide-bandwidth pulses, enabling long-range detection and penetrating obstacles. This work presents an in-depth review of UWB radar systems for recognizing human activities in a room and through-the-wall (TTW) with other diverse applications. After briefly discussing different UWB radar working principles and architectures, the study explores their role in various TTW applications in real-world scenarios. An extensive performance comparison of the legacy studies is presented, focusing on detection tools, signal processing, and imaging algorithms. The discussion includes an analysis of the integration of machine learning models. The primary focus is on the detection, movement, monitoring of vital signs, and nonhuman classifications in the context of Through-The-Wall (TTW) scenarios. This study contributes to a better understanding of evolving technology capabilities by integrating artificial intelligence (AI) and robotics to automate and precisely locate the target in various scenarios. Furthermore, the discussion includes the impact of UWB technology on society, future industry trends, the commercial landscape, and ethical issues to understand and future research.

In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems.

In recent years, biometric radar has gained increasing attention in the field of non-touch vital sign monitoring due to its high accuracy and strong ability to detect fine-grained movements. However, most current research on biometric radar can only achieve heart rate or respiration rate monitoring in static environments, which have strict monitoring requirements and single monitoring parameters. Moreover, most studies have not applied the collected data despite their significant potential for applications. In this paper, we introduce a non-touch motion-robust vital sign monitoring system via ultra-wideband (UWB) radar based on deep learning. Nmr-VSM not only enables multi-dimensional vital sign monitoring under human motion environments but also implements cardiac anomaly detection. The design of Nmr-VSM includes three key components. Firstly, we design a UWB radar that can perform multi-dimensional vital sign monitoring, including heart rate, respiratory rate, distance, and motion status. Secondly, we collect real experimental data and analyze the impact of eight factors, such as motion status and distance, on heart rate monitoring. We then propose a deep neural network (DNN)-based heart rate data correction model that achieves high robustness in motion environments. Finally, we model the heart rate variability (HRV) of the human body and propose a convolutional neural network (CNN)-based anomaly detection model that achieves low-latency detection of heart diseases, such as ventricular tachycardia and ventricular fibrillation. Experimental results in a real environment demonstrate that Nmr-VSM can not only accurately monitor heart rate but also achieve anomaly detection with low latency. [ABSTRACT FROM AUTHOR]

In recent years, biometric radar has gained increasing attention in the field of non-touch vital sign monitoring due to its high accuracy and strong ability to detect fine-grained movements. However, most current research on biometric radar can only achieve heart rate or respiration rate monitoring in static environments, which have strict monitoring requirements and single monitoring parameters. Moreover, most studies have not applied the collected data despite their significant potential for applications. In this paper, we introduce a non-touch motion-robust vital sign monitoring system via ultra-wideband (UWB) radar based on deep learning. Nmr-VSM not only enables multi-dimensional vital sign monitoring under human motion environments but also implements cardiac anomaly detection. The design of Nmr-VSM includes three key components. Firstly, we design a UWB radar that can perform multi-dimensional vital sign monitoring, including heart rate, respiratory rate, distance, and motion status. Secondly, we collect real experimental data and analyze the impact of eight factors, such as motion status and distance, on heart rate monitoring. We then propose a deep neural network (DNN)-based heart rate data correction model that achieves high robustness in motion environments. Finally, we model the heart rate variability (HRV) of the human body and propose a convolutional neural network (CNN)-based anomaly detection model that achieves low-latency detection of heart diseases, such as ventricular tachycardia and ventricular fibrillation. Experimental results in a real environment demonstrate that Nmr-VSM can not only accurately monitor heart rate but also achieve anomaly detection with low latency.

Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset. [ABSTRACT FROM AUTHOR]

Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.

Developments in sensing technology have shown that UWB radars are becoming increasingly valuable sensing devices that can be used for monitoring of humans in military/police and civilian areas. It is known that the applicability of particular methods of human localization depends on the character of persons’ motion. With respect to this finding, the researchers’ attention has been aimed at two fundamental directions. While the former is focused on the localization of moving persons (MP), the latter approach is intended to localize static persons (SP). Then, a proper fusion of the methods developed for MP and SP localization allows monitoring of persons moving with an unknown time-variable character of motion (MP-SP). The analyses of the currently known methods of MP and SP localization in terms of their use for MP-SP localization have shown that while MP localization methods are in principle well developed, SP localization methods are not sufficiently adapted for their use in MP-SP monitoring. Motivated by these findings, we would like to introduce a new radar signal processing procedure for SP localization (SPL) that could be an efficient component of algorithms to be applied for MP-SP monitoring. The novel features of the proposed SPL consist especially in a new approach to SP detection and inclusion of SP tracking in SPL. Moreover, SPL is characterized by relatively low computational complexity and is, therefore, suitable for real-time implementation. Experimental results have shown that SPL introduced in this paper provides very good performance for multiple person localization for line-of-sight and through-the-wall scenarios. [ABSTRACT FROM AUTHOR]

Distinguishing between humans and common animals through a wall is necessary for facilitating successful rescue of survivors and enhancing the confidence of rescuers in post-disaster search and rescue operations. However, few existing solutions are available with only dogs considered in this scenario. This poses an issue in ensuring the recognition accuracy involving different animal species. This work proposed a novel multiscale residual attention network for distinguishing between stationary humans and common animals under a through-wall condition based on ultra-wideband radar, which is yet to be performed by existing research using deep learning. Humans, dogs, cats, rabbits, and no target data are collected and distinguished. The overall architecture of the proposed method differed from conventional deep learning methods as it is constructed by parallel 3 × 3 and 5 × 5 kernels incorporated with the residual attention learning mechanism. The effect of the slow-time dimension on the classification performance is analyzed, thereby producing an optimal input size. The overall F1-score of the proposed network can reach a high value of 0.9064 and the recognition accuracy of human targets can reach 0.983 satisfying the requirements for post-disaster rescue. Then, the effectiveness and advancement of the three components of the overall network architecture are validated by ablation studies. Finally, the proposed method is compared with three state-of-the-art methods. Comparison results indicate that the proposed method achieve a better performance. The network and its results are envisioned to be applied in various practical situations, such as earthquake rescue and intelligent homecare.

In this paper, we propose a robust detection method to determine the presence of individuals in an indoor environment by exploiting an impulse-radio ultra-wideband (IR-UWB) radar. Detection of the presence of individuals in an indoor environment using IR-UWB is not a trivial problem because real indoor environments provide heavy clutter components and spurious ones due to multipath effects. The study mainly discusses two difficulties involved in detecting an individual in an indoor environment, namely, how to reduce the clutter in an indoor environment and how to detect a very low radar cross section (RCS) target, i.e., an individual lying down. To reduce clutter components in indoor environments, we investigated several clutter reduction techniques in terms of detecting a standing individual and an individual lying down. However, even after clutter reduction, detection of an individual lying down continues to pose a challenge. Thus, we devised a novel two-stage detection scheme that first involves detection in the range domain, and then in the frequency domain, thereby resulting in good detection performance in terms of a high detection rate and a low false alarm rate. The proposed method was demonstrated by experiments in indoor environments, and the results indicate that its performance is robust in various scenarios.

Detection and localization of nonline-of-sight (NLOS) hidden target in complex urban environment is a very recent radar detection problem. In this article, we investigate the possibility of locating a target in a building corner NLOS region with a single-input single-output ultra-wideband radar by exploring the electromagnetic (EM) diffraction propagation path. The EM wave diffraction phenomenon referring to this single rectangular corner of the building is analyzed and the diffraction model in this scenario is established. Assuming that the geometric structure of the scenario is known earlier, we introduce an application of the destination location behind the corner based on the propagation of the EM wave diffraction. Finally, the numerical simulation and real data results demonstrate that a micromotion target and moving target can be localized exploiting the proposed approach.

Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.

Radio frequency interference (RFI) is a critical problem for ultra-wideband synthetic aperture radars (UWB SAR) because the VHF/UHF band used by them is shared by other systems as well. A number of solutions have been proposed over the years. Recently, sparsity and low-rank estimation-based solutions were shown to perform better than traditional methods such as adaptive notch filtering. These algorithms model the SAR signal to be sparse and the RFI to be either sparse or low-ranked in nature and solve an optimization problem to estimate the SAR signal and RFI simultaneously. Algorithms in this class share the common characteristic that the SAR signal sparsity is captured by modeling each data vector as a linear combination of shifted SAR pulses. This data model addresses the structure of SAR signals in the down-range direction, but the inter-aperture cross-range structure, i.e., the fact that SAR signals add coherently across the cross-range, has been completely ignored. In this work, we incorporate this “global” 2-D structure of the SAR data into the RFI mitigation problem. Two algorithms are proposed: 1) (2-D) sparse SAR and sparse RFI estimation and 2) (2-D) sparse SAR and low-rank RFI estimation. The experimental results demonstrate that the 2-D model does a much better job of capturing the sparsity of SAR and the 2-D algorithms consistently perform better than the “local” 1-D algorithms. The level of improvement rises significantly in challenging cases—when the noise level and/or the number of RFI bands increases. Experiments are conducted extensively on simulated data sets as well as real SAR and RFI data sets collected by the U.S. Army Research Laboratory (ARL) to validate the proposed framework. [ABSTRACT FROM AUTHOR]

We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds. [ABSTRACT FROM AUTHOR]

The noncontact measurement of vital sign signals is useful for medical care, rescuing disaster survivors from ruins and public safety. In this paper, a novel vital sign signal extraction method based on permutation entropy (PE) and ensemble empirical mode decomposition (EEMD) algorithm is proposed. The proposed algorithm analyzes the permutation entropy of radar-received pulses; the range between a human target and ultra-wideband (UWB) radar can be obtained by permutation entropy. Permutation entropy represents the complexity of signals, so we can use PE to select and recombine human life signals that are distributed in the adjacent distance gate. Moreover, EEMD algorithm is adopted to decompose the combined signal into intrinsic mode functions (IMF), and both the respiration and the heartbeat signals are reconstructed by IMF via reaching the energy threshold in the time domain. Experiments are carried out using UWB radar. Compared with traditional algorithms, the proposed algorithm can be used to extract the range and frequency information of human targets efficiently and accurately.

Ultra-WideBand (UWB) radar can reconstruct the layout of a building, providing rich information for detecting and locating humans in buildings. Traditional imaging methods suffer from serious sidelobes and location displacement of behind-the-wall target because of the influence of walls. Sparse recovery is introduced into the field of through-the-wall imaging to improve the imaging quality. However, the reconstruction probability of weak scattering targets is low in traditional methods. In this study, the combination of sparse recovery method and Coherence Factor (CF) weighting is proposed to improve the reconstruction probability of weak scattering targets inside a room. The quasi-establishment of the support set can be improved during sparse imaging by reducing the effect of the sidelobes of strong scattering targets with CF, ultimately enhancing the robustness of the sparse imaging of the building layout. A location correction model for multiple walls after sparse imaging is established, based on which the locating error of walls can be reduced with a low amount of calculation. The results of the measured data reveal that compared with the traditional generalized orthogonal matching pursuit method, the proposed methods can improve the reconstruction probability of weak scattering targets and reduce the locating error of the inner layouts of buildings to less than 10 cm.

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car. [ABSTRACT FROM AUTHOR]

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

Contactless detection of human beings via extracting vital sign features (VSF) is a perfect technology by employing an ultra-wideband radar. Only using Fourier transform, it is a challenging task to extract VSF in a complex environment, which can cause a lower signal to noise ratio (SNR) and significant errors due to the harmonics. This paper proposes an improved signal processing algorithm for VSF extraction via analyzing the skewness and standard deviation of the collected impulses. The discrete windowed Fourier transform technique is used to estimate the time of arrival of the pulses. The frequency of human breathing movements is obtained using an accumulation scheme in frequency domain, which can better cancel out the harmonics. The capabilities of removing clutters and improving SNR are validated compared with several well-known methods experimentally. [ABSTRACT FROM AUTHOR]

This article deals with the problems on the time delay estimation and the vital jiggle signal detection of the stationary human target via the contactless ultra-wide band (UWB) radar. The time delay of the transmitted pulses between the radar and human target can be estimated based on the energy blocks acquired from the analyzed skewness and kurtosis. Using the index of the time delay estimation, the vital jiggle signal can be selected from the resultant matrix, which can better enhance signal to noise ratio (SNR). The feature parameter of the vital jiggle signal can be measured. Results obtained from five volunteers and one actuator show the detection capability of the designed technique. [ABSTRACT FROM AUTHOR]

Time reversal (TR) relies on the reciprocity nature of the wave equation. Waves diverging away from a source can be time reversed and back-propagated to focus back at the source. The transmitting and receiving antenna arrays utilized for TR are collectively termed as the TR mirror (TRM). TR imaging in the microwave regime has shown its promise in nondestructive inspection and target localization. This paper deals with the design of a microwave TRM for the detection of breast tumors. The first part of this paper discusses TR simulations based on a signal processing algorithm, to obtain the tumor location from the total fields. The processed time-reversed fields can be back-propagated in a numerical model to focus the wave at the tumor locations. Simulation results aid in investigating the feasibility and the robustness of the method. The second part of this paper deals with experimental studies for the breast tumor detection, using a pulsed time-domain system and the back-propagation model. The successful detection of single and multiple tumors embedded in a liquid breast tissue-based phantom is shown using the microwave system. [ABSTRACT FROM AUTHOR]

The precise knowledge of the statistical properties of clutter plays a key role in radar target detection in foliage environments. Owing to a wealth of multiple scattering, the selection of an appropriate model for diverse foliage environments remains a conundrum. Thus the statistical modeling of the foliage clutter in different environments is focused on in this paper. In particular, the primary contribution is to characterize and parameterize the amplitude of the foliage clutter in three typical foliage environments of different densities, including line-of-sight (LOS) forest environment, non line-of-sight (NLOS) forest-penetration environment, and NLOS bush-penetration environment. Based on the real clutter data acquired with an ultrahigh frequency (UHF) ultra-wideband (UWB) ground-based radar, the experimental results show that the specific foliage type and configuration has a significant impact on the distribution of foliage clutter. Furthermore, by means of Kullback-Leibler distance (KLD), log-logistic, G0 and G distributions with estimated parameters are demonstrated to be the best choice to model the clutter in the LOS forest environment, NLOS forest-penetration environment and NLOS bush-penetration environment, respectively. The proposed models are great advantages on understanding and prediction of the clutter in foliage environments. [ABSTRACT FROM AUTHOR]

It is challenging to extract signs of life under low signal to noise ratio (SNR) using a contactless radar in complex environments. Via analyzing the characteristics of signs of life, a feasible localization and detection technique is investigated by employing the through-wall ultra-wide band (UWB) radar. Based on the time-frequency matrix from the wavelet transform (WT) technique performed on the skewness and kurtosis of the received pulses, the distance from the radar to human beings is acquired. Human breathing rate (BR) is measured only using the pulses in the effective area. Different experiments are conducted to test the presented detection technique compared with several traditional methods. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the vital signals. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise, are wanted to be separated clearly. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. To solve this problem, a novel method is provided, in this paper, to extract the heartbeat and the breath information simultaneously. The method uses the feature time index with the first valley peak of the energy function of intrinsic mode functions (FVPIEF) calculated by pseudo bi-dimension ensemble empirical mode decomposition method and extracts the vital signals by the ensemble empirical mode decomposition (EEMD). Both simulation and experiment results evidently show that the proposed FVPIEF based two-layer EEMD method is effective for separating the small heartbeat signal from the large breath signal and significantly improves the evaluation of heart and breathing rates in both hold-breathing and breathing conditions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present simulation results of monostatic configuration for microwave imaging system by using confocal algorithm to detect and localize metallic bars inserted in a concrete pillar. An ultra‐wideband antenna has been used. The dielectric property values of the model were chosen near those of the realities. However, we have changed the permittivity of the concrete pillar to study the effectiveness of the algorithm despite the introduction of different kind of concrete. Successful detection and localization of different shapes and positions of metallic bars of diameter as small as 4 mm are presented. The efficacy of the proposed algorithm was also demonstrated by adding noise to the backscattered signals before creating the image. [ABSTRACT FROM AUTHOR]

In this paper, ultra-wideband (UWB) Doppler radar signatures from walking human subjects are processed with the state-space method (SSM) to analyze compressed radar echoes for the first time. A new mathematical model is proposed to detect the human walking strides using UWB radar, while micro-Doppler ( $\mu $ -D) features are extracted for gait analysis using short-time SSM (STSSM). To distinguish $\mu $ -D signatures of different subjects’ body parts, the SSM used for the characterization of radar target signatures and sensor fusion is applied on a sliding short-time window to enhance the resolution of feature extraction from data collected on a dismount. This method of the application of SSM to sliding short-time data is validated with a full-wave electromagnetic (EM) simulation of a walking subject using the Boulic model that represents the human kinematics. An EM scattering model is then utilized to compare the performance of a short-time Fourier transform with STSSM. Experimental results show that STSSM can be successfully applied to identify multiple $\mu $ -D trajectories in real experimental data, thus demonstrating the capability to positively identify human motions (right foot, left foot, and torso) even in a low signal-to-noise ratio environment. [ABSTRACT FROM AUTHOR]

We experimentally demonstrate a chaos through-wall imaging radar using ultra-wideband chaotic-pulse-position modulation (CPPM) microwave signal. The CPPM signal based on logistic map with 1-ns pulse width and 1-GHz bandwidth is implemented by a field programmable gate array (FPGA) and then up-converted as the radar transmitting signal. Two-dimensional image of human objects behind obstacles is obtained by correlation method and back projection algorithm. Our experiments successfully perform through-wall imaging for single and multiple human objects through 20-cm thick wall. The down-range resolution of the proposed radar is 15 cm. Furthermore, the anti-jamming properties of the proposed radar in CPPM jamming, linear frequency-modulated jamming, and Gaussian noise jamming environments are demonstrated by electromagnetic simulations using the finite-difference time-domain. The simulation results show the CPPM microwave signal possesses excellent jamming immunity to the noise and radio frequency interference, which makes it perform superbly in multiradar environments. [ABSTRACT FROM AUTHOR]

Two-Dimensional (2D) projection in temporal-Doppler plan is utilized to solve the effects of range-Doppler coupling and Doppler dispersion on radial velocity estimation for Linear Frequency Modulation (LFM) Ultra-WideBand (UWB) radar. First, temporal-Doppler 2D plane is got via 2D Fourier transform in fast-slow time domain. Second, 2D projection is performed to obtain energy focusing in UWB Doppler domain through mapping the UWB signal into narrow signal. Finally, radial velocities of moving targets are obtained according to the Doppler frequencies of the narrow signal after the mapping. This method not only solves the negative impacts of UWB radar Doppler dispersion on velocity estimation, but also voids that energy can not be accumulated in slow time since targets move along range resolution bins. Range profile of targets can also be got via the corresponding compensation with the estimated velocities. Besides, the velocity restriction is further derived according to the corresponding requirements. The simulation results illustrate the effectiveness of this method.

The specific advantages of ultra-wideband electromagnetic remote sensing (UWB radar) make it a particularly attractive technique for biomedical applications. We partially review our activities in utilizing this novel approach for the benefit of high and ultra-high field magnetic resonance imaging (MRI) and other applications, e.g., for intensive care medicine and biomedical research. We could show that our approach is beneficial for applications like motion tracking for high resolution brain imaging due to the non-contact acquisition of involuntary head motions with high spatial resolution, navigation for cardiac MRI due to our interpretation of the detected physiological mechanical contraction of the heart muscle and for MR safety, since we have investigated the influence of high static magnetic fields on myocardial mechanics. From our findings we could conclude, that UWB radar can serve as a navigator technique for high and ultra-high field magnetic resonance imaging and can be beneficial preserving the high resolution capability of this imaging modality. Furthermore it can potentially be used to support standard ECG analysis by complementary information where sole ECG analysis fails. Further analytical investigations have proven the feasibility of this method for intracranial displacements detection and the rendition of a tumour’s contrast agent based perfusion dynamic. Beside these analytical approaches we have carried out FDTD simulations of a complex arrangement mimicking the illumination of a human torso model incorporating the geometry of the antennas applied.

In this paper, the design of pulse characteristics to achieve the desired image resolution for near-field synthetic aperture radar is presented. Gaussian and chirp pulses, which are the most commonly used pulses for ultra-wideband (UWB) radar applications, are considered in this paper. The effect of the pulse shape, bandwidth, integration angle, and signal-to-noise ratio (SNR) of the received pulse on the image resolution is comprehensively studied. To enhance the image resolution, preprocessing of the received pulses with envelope detection or match filtering are also studied. The range and cross-range resolutions achieved by Gaussian and chirp pulses with the same center frequency and bandwidth at various SNR values are compared. This paper shows that the Gaussian pulse with envelope detection provides better image resolution, whereas the chirp pulse with match filtering provides more resistance to noise. Closed-form equations and design guidelines are developed to design the input pulse characteristics to achieve the desired image resolution. The antennas’ effect on UWB pulses and the developed equation for cross-range resolution, are both validated using full-wave simulations and measurements. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel localization framework which combines the detection results of multiple radars in a distributed heterogeneous radar sensor network. The radar sensor network consists of multiple ultra-wideband (UWB) and frequency-modulated continuous-wave (FMCW) radars, distributed across the region of detection. The UWB radar is used because of its excellent time resolution, which leads to the estimation of distance at a fine resolution. On the other hand, the FMCW radar is used because it can provide velocity and angle measurements in clutter-rich environment. The angle measurement is incorporated into the conventional range-based trilateration algorithm to improve the localization accuracy. In addition, because each radar has a different measurement capability, different weights are assigned to each measurement when deriving the least-squares solution. The performance of the proposed method is demonstrated using experimental data measured in an open space and a clutter-rich environment. The localization results showed that the fusion of UWB and FMCW radar data leads to enhanced localization performance. • Localization framework using joint UWB and FMCW radar data • Range and angle measurements of heterogeneous radar sensor network are used • Clutter removal, clustering, and weight assignment based on the type of radar • Performance evaluation in an open space and a clutter-rich environment [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) radar plays an important role in search and rescue at disaster relief sites. Identifying vital signs and locating buried survivors are two important research contents in this field. In general, it is hard to identify a human's vital signs (breathing and heartbeat) in complex environments due to the low signal-to-noise ratio of the vital sign in radar signals. In this paper, advanced signal-processing approaches are used to identify and to extract human vital signs in complex environments. First, we apply Curvelet transform to remove the source–receiver direct coupling wave and background clutters. Next, singular value decomposition is used to de-noise in the life signals. Finally, the results are presented based on FFT and Hilbert-Huang transform to separate and to extract human vital sign frequencies, as well as the micro-Doppler shift characteristics. The proposed processing approach is first tested by a set of synthetic data generated by FDTD simulation for UWB radar detection of two trapped victims under debris at an earthquake site of collapsed buildings. Then, it is validated by laboratory experiments data. The results demonstrate that the combination of UWB radar as the hardware and advanced signal-processing algorithms as the software has potential for efficient vital sign detection and location in search and rescue for trapped victims in complex environment. [ABSTRACT FROM PUBLISHER]

Via analyzing the characteristics of life sign, a novel method is proposed to detect multiple trapped victims by ultra-wideband radar in this letter. The signal-to-noise and clutter ratio (SNCR) is greatly improved. The non-static clutter can be removed effectively. The number, range position and respiratory frequency of life signs can be detected automatically and accurately in low SNCR environment. By using experiment and numerical simulation, the good performance of the new method is proved. [ABSTRACT FROM PUBLISHER]

A prototype microwave breast imaging system is used to scan a small group of patients. The prototype implements a monostatic radar-based approach to microwave imaging and utilizes ultra-wideband signals. Eight patients were successfully scanned, and several of the resulting images show responses consistent with the clinical patient histories. These encouraging results motivate further studies of microwave imaging for breast health assessment. [ABSTRACT FROM AUTHOR]

In this paper, a method to characterize buried nonmagnetic objects in ground using ultra-wideband (UWB) ground penetrating radar (GPR) is proposed. In this method, UWB pulses are radiated by the radar, while scattered signals from the ground with the buried object are received. The received signals are then post-processed to estimate the depth, thickness, and electrical properties of the buried object. A constant depth and thickness is enforced at all frequencies while the signals are processed to extract the buried object characteristics, resulting in more accurate estimations and reduced processing time. In addition, path loss due to the close proximity of the radar to the ground is compensated analytically. The applicability of the proposed method is validated with several planar objects and a boulder that we typically encounter in the construction industry. The proposed method can achieve sufficient reliability in estimating the permittivity of buried objects for the purpose of material identification. Incorporating the proposed method into the GPRs enhances their existing imaging ability by adding material identification capability. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) radar with its high range resolution and applicability to optically harsh environments, offer great promise for near field sensing systems. It is particularly suitable for robotic or security sensors that must identify a target in low visibility. Some recently developed radar imaging algorithms proactively employ multiple scattered components, which can enhance an imaging range compared to synthesizing a single scattered component. We have already proposed the synthetic aperture radar (SAR) method considering a double scattered, which successfully expanded a reconstructible range of radar imagery with no a priori knowledge of target or surroundings. However, it requires a multiple integration of the received signals, requiring the fifth times integration in the 3-D case. Thus, this method requires an intensive computation and its spatial resolution is insufficient for clear boundary extraction such as edges or specular surfaces. As a substantial solution, this paper proposes a novel shadow region imaging algorithm based on a range derivative of double scattered signals. This new method accomplishes high-speed imaging, including a shadow region without any integration process, and enhances the accuracy with respect to clear boundary extraction. Results from numerical simulations verify that the proposed method remarkably decreases the computation amount compared to that for the conventional method, especially for the 3-D problem, enhancing the visible range of radar imagery. [ABSTRACT FROM PUBLISHER]

The specific advantages of ultra-wideband electromagnetic remote sensing (UWB radar) make it a particularly attractive technique for biomedical applications. We partially review our activities in utilizing this novel approach for the benefit of high and ultra-high field magnetic resonance imaging (MRI) and other applications, e.g., for intensive care medicine and biomedical research. We could show that our approach is beneficial for applications like motion tracking for high resolution brain imaging due to the non-contact acquisition of involuntary head motions with high spatial resolution, navigation for cardiac MRI due to our interpretation of the detected physiological mechanical contraction of the heart muscle and for MR safety, since we have investigated the influence of high static magnetic fields on myocardial mechanics. From our findings we could conclude, that UWB radar can serve as a navigator technique for high and ultra-high field magnetic resonance imaging and can be beneficial preserving the high resolution capability of this imaging modality. Furthermore it can potentially be used to support standard ECG analysis by complementary information where sole ECG analysis fails. Further analytical investigations have proven the feasibility of this method for intracranial displacements detection and the rendition of a tumour's contrast agent based perfusion dynamic. Beside these analytical approaches we have carried out FDTD simulations of a complex arrangement mimicking the illumination of a human torso model incorporating the geometry of the antennas applied. [ABSTRACT FROM AUTHOR]

A new image reconstruction algorithm, termed as delay-multiply-and-sum (DMAS), for breast cancer detection using an ultra-wideband confocal microwave imaging technique is proposed. In DMAS algorithm, the backscattered signals received from numerical breast phantoms simulated using the finite-difference time-domain method are time shifted, multiplied in pair, and the products are summed to form a synthetic focal point. The effectiveness of the DMAS algorithm is shown by applying it to backscattered signals received from a variety of numerical breast phantoms. The reconstructed images illustrate improvement in identification of embedded malignant tumors over the delay-and-sum algorithm. Successful detection and localization of tumors as small as 2 mm in diameter are also demonstrated. [ABSTRACT FROM AUTHOR]

The extinction pulse (E-pulse) technique has been widely applied to the problem of free-space radar target identification. In this paper, the possibility of applying the same E-pulse technique to a subsurface target recognition scheme is investigated. In particular, the detection of depth changes of a metallic target is considered with numerical examples that determine the depth of a hip prosthesis model buried inside representative human tissue. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel amplitude-comparison monopulse receiver architecture for ultra-wideband radars. This monopulse receiver consists of four ridged-horn antennas placed in a square-feed configuration, a comparator circuit that generates the monopulse sum and difference signals, cross-correlation receivers that detect the monopulse signals, and an amplitude-comparison monopulse processor that determines the target's angular position. The derived monopulse sum and difference signals are verified through measurements. The derived sum and difference patterns are compared with measured patterns, and they show good agreements--measured 3-dB beamwidth = 6.4°(derived = 6°), measured unambiguous tracking range = ±5°(derived = ±5°), and measured sum pattern sidelobe level = -6 dB (derived = -8 dB). [ABSTRACT FROM AUTHOR]

We present a computational study comparing the performance of narrowband (NB) microwave hyperthermia for breast cancer treatment with a recently proposed ultra-wideband (UWB) approach. Space-time beamforming is used to preprocess input signals from both UWB and NB sources. The train of UWB pulses or the NB sinusoidal signals are then transmitted simultaneously from multiple antennas into the breast. Performance is evaluated using finite-difference time-domain electromagnetic (EM) and thermal simulations with realistic numerical breast phantoms derived from magnetic resonance images (MRIs) of the breast. We use three methods of mapping MRI data to complex permittivity data to account for uncertainty in the embodiment of the dielectric properties transitions in heterogeneous breast tissue. EM power-density deposition profiles and temperature profiles are compared for the UWB and NB cases in the three different breast phantoms. Dominant mechanisms that influence the efficacy of focusing UWB and NB signals in the breast are identified. The results of this study suggest that, while NB focusing performs reasonably well when the excitation frequency is optimized, UWB focusing consistently performs better, offering the potential for tighter focusing and greater reduction of hot spots, particularly in breast tissue, which exhibits distinct dielectric-properties boundaries within the tissue heterogeneity. [ABSTRACT FROM AUTHOR]

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm. [ABSTRACT FROM AUTHOR]