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This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system., Comment: 8 pages, 11 figures, 5 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

In this study, we propose a method for generating an angular spectrum using array radar and physiological component analysis. We develop physiological component analysis to separate radar echoes from multiple body positions, where echoes are phase-modulated by propagating pulse waves. Assuming that the pulse wave displacements at multiple body positions are constant multiples of a time-shifted waveform, the method estimates echoes using a simplified mathematical model. We exploit the mainlobe and nulls of the directional patterns of the physiological component analysis to form an angular spectrum. We applied the proposed method to simulated data to demonstrate that it can generate a super-resolution angular spectrum.

In this study, we present a quantitative evaluation of the accuracy of simultaneous array-radar-based measurements of the displacements caused at two parts of the human body by arterial pulse wave propagation. To establish the feasibility of accurate radar-based noncontact measurement of this pulse wave propagation, we perform experiments with four participants using a 79-GHz millimeter-wave ultra-wideband multiple-input multiple-output array radar system and a pair of laser displacement sensors. We evaluate the accuracy of the pulse wave propagation measurements by comparing the displacement waveforms that are measured using the radar system with the corresponding waveforms that are measured using the laser sensors. In addition, to evaluate the estimates of the pulse wave propagation channels, we compare the impulse response functions that are calculated from the displacement waveforms obtained from both the radar data and the laser data. The displacement waveforms and the impulse responses both demonstrated the good agreement between the results of the radar and laser measurements. The normalized correlation coefficient between the impulse responses obtained from the radar and laser data on average was as high as 0.97 for the four participants. The results presented here strongly support the feasibility of accurate radar-based noncontact measurement of arterial pulse wave propagation.

The noncontact measurement of vital signs using ultra-wideband radar has been attracting increasing attention because it can unobtrusively provide information about the physical and mental condition of people. In particular, the continuous measurement of a person's time-varying instantaneous heart rate can estimate the activity level of the autonomic nervous system without the person wearing any sensors. Continuous heart rate measurement using radar is, however, a difficult task because accuracy is compromised by numerous factors, such as the posture and motion of the target person. In this study, we introduce techniques for increasing the accuracy and reliability of the noncontact measurement of heart rate variability. We demonstrate the performance of the proposed techniques by applying them to radar measurement data from a sleeping person, and we also compare its accuracy with electrocardiogram data.

Vital-sign estimation using ultra-wideband (UWB) radar is preferable because it is contactless and less privacy-invasive. Recently, many approaches have been proposed for estimating heart rate from UWB radar data. However, their performance is still not reliable enough for practical applications. To improve the accuracy, this study employs convolutional neural networks to learn the special patterns of the heartbeats. In the proposed system, skin displacements of the target person are measured using UWB radar, and the radar signal is converted to a two-dimensional matrix, which is used as the input of the designed neural networks. Meanwhile, two triangular waves corresponding to the peaks and valleys in an electrocardiogram are adopted as the output of the networks. The proposed system then identifies each individual and estimates the heart rate automatically based on the already trained neural networks. The estimation error of the interbeat interval computed using our approach was reduced to 4.5 ms in the best case; and 48.5 ms in the worst case. Experiment results show that the proposed approach significantly outperforms a conventional method. The proposed machine learning approach achieves both personal identification and heart rate estimation simultaneously using UWB radar data for the first time. Moreover, this study found that using the respiration and heartbeat components together may enhance the accuracy of heart rate estimation, which is counter-intuitive, because the respiration is usually believed to interfere with the heartbeat.

Adaptive signal processing techniques can estimate the directions of arrival (DOAs), ranges, and radial velocities of radar targets with high resolution when applied in the space, frequency, and time domains, respectively. However, the performance of these techniques is limited when they are applied separately in each domain. Recently, adaptive signal processing algorithms based on high-dimensional signal subspaces have been studied extensively. We apply a four-dimensional unitary estimation of signal parameters via rotational invariant techniques (ESPRIT) algorithm for the simultaneous estimation of the DOAs, ranges, and Doppler velocities of multiple targets in an ultra-wideband radar imaging setting and show the effectiveness of the high-dimensional ESPRIT for the near-field imaging of distributed moving targets. The four-dimensional ESPRIT algorithm is demonstrated to be able to separate targets moving in close proximity, whereas the two- and three-dimensional ESPRIT algorithms fail to separate these targets accurately because of their limited resolutions. The application of the high-dimensional ESPRIT to near-field radar imaging covers a wide range of applications that require the measurement of multiple moving targets in close proximity. An example of such an application is radar-based human monitoring. Therefore, the superior resolution of the high-dimensional ESPRIT has the potential to improve the performance of various real-world security and healthcare systems.

We developed a noncontact measurement system for monitoring the respiration of multiple people using millimeter-wave array radar. To separate the radar echoes of multiple people, conventional techniques cluster the radar echoes in the time, frequency, or spatial domain. Focusing on the measurement of the respiratory signals of multiple people, we propose a method called respiratory-space clustering, in which individual differences in the respiratory rate are effectively exploited to accurately resolve the echoes from human bodies. The proposed respiratory-space clustering can separate echoes, even when people are located close to each other. In addition, the proposed method can be applied when the number of targets is unknown and can accurately estimate the number and positions of people. We perform multiple experiments involving five or seven participants to verify the performance of the proposed method, and quantitatively evaluate the estimation accuracy for the number of people and the respiratory intervals. The experimental results show that the average root-mean-square error in estimating the respiratory interval is 196 ms using the proposed method. The use of the proposed method, rather the conventional method, improves the accuracy of the estimation of the number of people by 85.0%, which indicates the effectiveness of the proposed method for the measurement of the respiration of multiple people., This paper has been published in IEEE Access (Early Access), 13 pages, 24 figures, 1 table

This study evaluates the accuracy of measurements of cardiac pulse wave propagation using a 79-GHz millimeter-wave ultra-wideband multiple-input multiple-output array radar. The radar echoes from multiple parts of the human body were extracted using an array beamforming technique. The phase of each echo was converted to the skin displacement and its waveform was carefully compared with the waveform obtained using laser displacement sensors, which was used as a reference for evaluation of accuracy. Because the beam spot sizes of the radar and laser sensors were different, we applied smoothing to the waveform measured using the laser sensor, to make the effective beam spot size equivalent to the spot size of the array radar beam. We then quantitatively analyzed the relationship between body displacement waveforms measured using the array radar and laser sensors.

Early diagnosis of sleep-apnea-related breathing problems helps to avoid the increased risk they can cause. In this study, we performed simultaneous radar measurements and polysomnography on patients with sleep apnea. A support vector machine algorithm was applied to the radar data to automatically detect sleep apnea events. Support vector machine parameters were optimized using the relationship between the radar and polysomnography data. The support vector machine was found to be effective in noncontact detection of central/mixed sleep apnea events using radar data. The proposed approach achieved an accuracy of 79.5%, a recall of 71.2%, and a precision of 71.2%.

The arterial pulse wave, which propagates along the artery, is an important indicator of various cardiovascular diseases. By measuring the displacement at multiple parts of the human body, pulse wave velocity can be estimated from the pulse transit time. This paper proposes a technique for signal separation using an antenna array, so that pulse wave propagation can be measured in a non-contact manner. The body displacements due to the pulse wave at different body parts are highly correlated, and cannot be accurately separated using techniques that assume independent or uncorrelated signals. The proposed method formulates the signal separation as an optimization problem, based on a mathematical model of the arterial pulse wave. The objective function in the optimization comprises four terms that are derived based on a small-displacement approximation, unimodal impulse response approximation, and a causality condition. The optimization process was implemented using a genetic algorithm. The effectiveness of the proposed method is demonstrated through numerical simulations and experiments., Comment: This paper has been published in IEEE Access (Early Access), 12 pages, 17 figures

We propose a hand gesture recognition technique using a convolutional neural network applied to radar echo inphase/quadrature (I/Q) plot trajectories. The proposed technique is demonstrated to accurately recognize six types of hand gestures for ten participants. The system consists of a low-cost 2.4-GHz continuous-wave monostatic radar with a single antenna. The radar echo trajectories are converted to low-resolution images and are used for the training and evaluation of the proposed technique. Results indicate that the proposed technique can recognize hand gestures with average accuracy exceeding 90%.

A transformer type field shielding superconducting fault current limiter (SFCL), which is made of rare-earth barium-copper-oxide (REBCO) for primary and secondary windings, is designed and made for demonstration of not only fault current limiting but also power system stability improvement performance. A lab-scale test power system consists of one machine (18.26 kVA, 200 V) and double transmission route to an infinite bus. The simulated fault occurs at the sending end of one of the transmission route and after a certain time (fault clearing time), the fault route was rejected by circuit breakers. While the clearing time is short, the generator can continue to operate with one healthy transmission route, however, it becomes longer than a certain time critical clearing time (CCT), the generator loses its synchronism and fails into step out. The test SFCL was installed at the generator side bus of the fault route. The peak fault current was reduced to less than half of that without SFCL and the generator terminal voltage did not fall below 20% of rated voltage. It was confirmed experimentally that CCT can be enhanced by introducing the SFCL. It is confirmed that the SFCL cannot only limit the fault current but also improve the system stability.

A low distortion dc coupled CW radar system with high signal to noise ratio is capable of accurate representation of respiration in human subjects. We propose to test the hypothesis that a non-contact physiological radar monitoring system which measures and characterizes subtle body kinematics, can be made to resolve patterns accurately enough to recognize an individual’s identity. This paper investigates a technique to attain the requisite signal to noise ratio by dc offset management. Detailed exploration of the unique features in respiration signals using noncontact CW Doppler radar are presented. A proposed dynamic segmentation technique allowed detection of various unique features and patterns. KMN nearest neighbor and majority vote algorithms were implemented in software for this radar-based unique identification system. The system was tested and validated for six test subjects with 95% success rate. Fractal analysis of minor components of linearly demodulated radar signal was also presented for additional improvement in accuracy. This paper is believed to be significant as radar unique identification of human subjects has many potential applications, including security, health monitoring, IoT applications, and virtual reality.

A noncontact measurement of the heart rate of a specific person using an ${X}$ -band radar system with a four-element antenna array is presented. The system comprises a 2-D planar wide-beam antenna array with a four-channel network analyzer. The direction of arrival is estimated using the Capon method, and the directionally constrained minimization-of-power algorithm is then applied to the received signals. Signals from the four channels are adaptively combined to enhance the accuracy in estimating the instantaneous heart rate in a multi-person scenario. Measurement results indicate that the proposed system with the adaptive array processing technique is effective for the noncontact measurement of the heart rate of a specific person when there is more than one person in the scene.

Ultrawideband Doppler radar interferometry is known as an effective method that enables high-resolution imaging when using a simple antenna array. The technique, however, suffers from image artifacts when multiple moving targets with the similar Doppler velocities are present in the same range bin. To resolve this problem, we combine the Doppler interferometry technique with the Capon methods. Through numerical simulations and experiments, we show the remarkable performance improvement achieved by the proposed method.

A method for estimating Doppler velocities using ultra-wideband radar data is presented. Unlike conventional time-frequency analysis, the proposed method can directly obtain Doppler velocities without searching for peaks in a spectrum. By exploiting closed-form solutions for the Doppler velocities, it avoids the trade-off between time and frequency resolution, thus maintaining high time resolution. Both simulations and measurements are used to evaluate the proposed method versus conventional techniques.

The feasibility of measuring heart rate using radar echoes from a human head is demonstrated. Non-contact measurement of vital signs using radar has been attracting much attention because such technologies can breakthrough benefits for monitoring health conditions without electrodes or wearable devices. Most existing studies have measured echoes from the torso, particularly the chest wall. However, this is difficult because of multiple interfering reflections from the complex shape of the torso and other body parts, such as limbs. The current study is the first demonstration that non-contact heart rate measurement can be easily achieved using echoes from the human head. There are two important advantages of measurement from the human head: (i) the simple shape of the head makes an ideal radar target with only a single reflection, and (ii) the other undesired echoes can be removed using time-gating when an ultra-wideband radar is used. Nonetheless, because the displacement of the human head due to heart rate is small, a millimetre-wave ultra-wideband array radar system is developed, which is installed on the ceiling and used in the proposed measurements with participants.

Radar-based noncontact measurements of human bodies have increasingly been attracting attention in healthcare applications because such measurements allow for the unobtrusive sensing of vital signs without sensors being attached to the body. In particular, measurement of the heart rate is considered to be important because it provides various types of information on the physical and mental health of the person under test. In such applications, it is preferable to use low-gain omni-directional antennas so that the person under test does not have to be located at a specific spot. In addition, the use of high-power microwaves is not allowed in many countries owing to public health regulations. Because of such factors, the signal-to-noise ratio is relatively low in these applications although the target person might be within a few meters of the antennas. To overcome this difficulty, we adopt the maximum ratio combining technique with a multiple-input multiple-output antenna array for improving the signal-to-noise ratio and accuracy in measuring the instantaneous heart rate.

We propose a fast method of ultrawideband (UWB) radar imaging that can be applied to a moving target, having in mind such application as concealed weapon detection. We demonstrate the performance of the proposed method using simulations and measurements with static and moving targets. We also compare the computational complexity of the proposed method with that of a conventional method to clarify the feasibility of applying the proposed method to the intended real-time systems.

Radar-based noncontact heart rate measurement is attracting increasing attention. However, the measurement accuracy is dependent on the position and posture of the target body. In this work, we generate radar echo waveforms using a physical optics approximation and a numerical human body model that was obtained using a depth camera. Millimeter-wave array radar is used to form an optimal antenna pattern directed towards the most dominant scattering center on the human body. We demonstrate that the proposed method can track a moving human body automatically and accurately, even when the body is in motion, thus enabling noncontact heartbeat measurement.

This study demonstrates the feasibility of human body imaging using an array radar system for monitoring vital signs. In this study, a depth camera is used to obtain a numerical three-dimensional model of a human body, which is used for electromagnetic scattering simulation. We apply physical optics approximation for numerical analysis, and the simulated radar echoes are processed to find the position of the scattering centers on the human body.

A novel concept, called range-Doppler surface (RDS), for human target analysis using ultra-wideband radar is proposed. The construction of RDS involves range-Doppler (RD) imaging, adaptive threshold detection and isosurface extraction. A Keystone-transform-based range migration compensation approach is proposed to allow high-quality RD imaging using ultra-wideband radar. Adaptive threshold detection is applied to detect the extended target in the RD image, and RDS is constructed by extracting an isosurface from a RD video sequence, which is defined as a sequence of RD images. In comparison with micro-Doppler profiles and high-resolution range profiles, RDS contains range, Doppler and time information simultaneously. An ellipsoid-based human motion model is designed for validation. RDSs simulated for different human activities are demonstrated and discussed. Finally, experimental results for single/two-people walking scenarios are presented to verify the simulation results. The use of the RDS opens a new area of human target analysis.

A novel algorithm is proposed for separating multiple moving targets in radar images in the slow time-range domain. Target discrimination is based on an image texture angle that is related to the target's instantaneous velocity. The algorithm efficiency has been successfully verified for targets with variable velocities.

To measure vital signs using Doppler radar, a common approach involves the use of time-varying echo phase. To acquire these measurements, clutter rejection is necessary because clutter power is often larger than echo power. To reject static clutter, several techniques have been proposed that assume relatively large phase rotation angles because many studies assume the measurement of the upper torso, where displacement is mainly caused by respiration. However, signals from other parts of the human body are known to have smaller displacements that exhibit small phase rotation angles, which make clutter rejection more difficult. Three clutter rejection techniques for measuring small displacements are compared and their performances are investigated. Using numerical analysis, one method is demonstrated to be the most effective, even for small displacements in noisy data. The best method successfully estimates the centre with an error of −13.4 and −24.0 dB, with a signal-to-noise ratio of 10 and 40 dB and the range of phase rotation angles of 90° and 15°.

The present paper proposes a technique for the automatic recognition of hand gestures using a 2.4-GHz continuous radar and a convolutional neural network. The proposed technique applies the neural network to the time-domain I-Q plot of radar echoes. The accurate recognition capability of the technique was established with a set of radar data for three types of hand gesture performed by a participant. The radar echo trajectories were converted to low-resolution images to achieve fast signal processing, and the neural network was trained using the images. Another set of I-Q plot images were used for the evaluation of the recognition accuracy. The results indicate that the proposed technique is able to recognize hand gestures with accuracy exceeding 90%.

This paper presents a radar imaging technique for breast cancer detection using an ultra-wideband radar. The proposed technique employed modified Kirchhoff migration to achieve high-resolution imaging. In addition, a singular value decomposition was used for noise suppression to obtain a clear image. The proposed technique was applied to experimental data measured using an ultra-wideband array radar system and breast cancer phantom.

For tracking multiple moving targets, a multi-radar system employing ultra-wideband millimeter-wave array antennas is introduced. To separate the multiple echoes and the different Doppler velocities of the targets, the frequency-domain interferometric technique is used and is followed by an interferometric imaging process. The system consists of multiple radar modules that are well-separated, allowing measurements of the Doppler velocities from different lines of sight. Thus, in addition to constructing high-resolution images, the multi-radar system is able to estimate the velocity vector of each target, which has been demonstrated in practice through measurements.

Ultra-wideband (UWB) radar imaging has attracted attention for use in security and intelligent transportation system (ITS) applications. Conventional UWB Doppler interferometry is an effective way to obtain high-resolution images while using a simple radar system. However, this method produces ghost images when multiple closely-spaced human targets are present. To resolve this problem, we propose a new technique that combines UWB Doppler interferometry with an adaptive beamforming method called estimation of signal parameters via rotational invariance techniques (ESPRIT). We also propose a tracking and separation algorithm that uses the k-nearest neighbor method. Through a combination of numerical simulations and measurements, we demonstrate the remarkable performance improvement that can be achieved using our proposed method. The proposed method can separate multiple humans with a root-mean-square error of 5.2 cm, which makes its accuracy 1.9 times higher than that of the conventional method.

Contactless respiration monitoring using Doppler radar is an important technology for healthcare applications. The radar measures small displacements of the body surface. In this study, we propose a new algorithm to separate multiple targets placed closely together at the same range but at different lateral positions using ultra-wideband array radar and the Capon method. The Capon method, which is an adaptive beamforming - technique, assumes that arrival echoes are not correlated. However, echoes from the human body should be correlated and this reduces system performance. We improve the performance - by introducing two different diagonal loading factor values for direction of arrival estimation and weight vector calculation. In an experimental study, the proposed method separates the echoes from two patients spaced at a lateral distance of approximately 70 mm. The estimated displacement error when using the proposed method is less than 0.13 mm, while that of the conventional method is 2.7 mm.

Recently, the authors proposed a fast high-resolution imaging algorithm, called frequency-wavenumber (F-K) Kirchhoff migration. It was developed by combining three algorithms: F-K migration, Kirchhoff migration and inverse boundary scattering transform. The F-K Kirchhoff migration is considered promising in improving the image resolution of images taken with airport security body scanners for which costs can be mitigated by introducing sparse arrays. We investigate the imaging performance of the F-K Kirchhoff migration using ultra-wideband radar with such sparse arrays.

Many people carry smartphones and tablets with them on a daily basis and are constantly connected via wireless. As the Internet of Things (IoT) comes of age, people increasingly want to use various devices in the surrounding environment in conjunction with their smartphones. The cost of developing services that cooperate with the devices is high because various kinds of devices exist and multiple OSs for smartphones need to be supported. To mitigate the issue, we propose a dynamic device connection method that can connect services (e.g. Web applications) with devices located close to users by installing the device drivers and/or protocol adapters dynamically. Developers can create new Web applications easily because the device drivers absorb the differences of the devices and provide a common Web interface for them. The protocol adapters provide access capability to OS application programming interfaces (APIs) for Web technology. We implemented the proposed method on an Android smartphone and evaluated it with Bluetooth Low Energy devices and a Classic Bluetooth device. Results showed that the method absorbs the difference of function and protocol and delivers a reasonable performance.

Ultra wideband (UWB) radar is considered a promising technology to complement existing camera-based surveillance systems because, unlike cameras, it provides excellent range resolution. Many of the UWB radar imaging algorithms are based on large-scale antenna arrays that are not necessarily practical because of their complexity and high cost. To resolve this issue, we previously developed a two-dimensional radar imaging algorithm that estimates unknown target shapes and motion using only three antennas. In this paper, we extend this method to obtain three-dimensional images by estimating three-dimensional motions from the outputs of five antennas. Numerical simulations confirm that the proposed method can estimate accurately the target shape under various conditions.

Ultrawideband (UWB) pulse radar with high range resolution is suitable for near-field sensing. Applications of UWB pulse radar include human body identification in blurry vision for security or rescue purposes and accurate spatial measurements for industrial products such as a reflector antenna. The synthetic aperture radar is still promising for these applications because it creates an accurate image even for near-field targets in free space. However, for complex-shaped or multiple objects, this algorithm suffers from increased shadow region because it employs only a single-scattered signal for imaging. To resolve this difficulty, this paper proposes a novel imaging algorithm based on aperture synthesis for double-scattered signals. In general, double-scattered waves include independent information on target points, which are not obtained by a single-scattered wave. Based on this principle, the proposed method effectively synthesizes the double-scattered signals and enhances the reconstructible range of a target shape, part of which becomes a shadow in the former approach. In order to enhance accuracy, a false image suppression approach based on the Fresnel zone theory is also incorporated in the proposed method. The results from numerical simulations and an experiment verify that our method significantly enhances the visible range of target surfaces without either a priori knowledge of target shapes or preliminary observation of their surroundings.

Indoor target detection and imaging technologies hold great interest for security surveillance systems. The ultrawideband (UWB) radar is promising because it can complement conventional camera-based systems. However, conventional UWB radar imaging systems are costly and impractical because they require large antenna arrays for acceptable resolution. This paper proposes a low-cost UWB radar imaging method using the motion of a target. The method employs five antennas for estimating the motion of a target, including its rotation, to obtain an image. Previous work deals only with a target in translation without rotation, which makes the method difficult to apply in practice. The proposed method, an extension of such previous methods, obtains an accurate image for an elliptical or distorted nonelliptical target with arbitrary translation and rotation. Numerical simulation and experimental results show that the proposed method is capable of accurately estimating motions and shapes.

Near field radar employing ultrawideband (UWB) signals with its high range resolution has great promise for various sensing applications. It enables non-contact measurement of precision devices with specular surfaces like an aircraft fuselage and wing, or a robotic sensor that can identify a human body in invisible situations. As one of the most promising radar algorithms, the range points migration (RPM) was proposed. This achieves fast and accurate surface extraction, even for complex-shaped objects, by eliminating the difficulty of connecting range points. However, in the case of a more complex shape whose variation scale is less than a pulsewidth, it still suffers from image distortion caused by multiple interference signals with different waveforms. As a substantial solution, this paper proposes a novel range extraction algorithm by extending the Capon method, known as frequency domain interferometry (FDI). This algorithm combines reference signal optimization with the original Capon method to enhance the accuracy and resolution for an observed range into which a deformed waveform model is introduced. The results obtained from numerical simulations and an experiment with bi-static extension of the RPM prove that super-resolution UWB radar imaging is accomplished by the combination between the RPM and the extended Capon methods, even for an extremely complex-surface target including edges.

UWB (Ultra Wide-Band) pulse radar is promising for surveillance systems because it has an outstanding high range-resolution. To realize an accurate UWB radar imaging system, we propose a new approach that employs multipath echoes from a target in an indoor environment. Using multipath echoes, the proposed system can accurately estimate images, even for targets in a shadow region where the targets are out of sight of the antenna. We apply a simple interferometry technique using the multiple mirror image antennas generated by multipath propagation. We find that this simple method also produces many undesired false image points. To tackle this issue, we also propose an effective false image reduction algorithm to obtain a clear image. Numerical simulations verify that most of the false image points are removed and the target shape is accurately estimated.

UWB (ultra wide-band) pulse radar is a promising candidate for surveillance systems. The fast SEABED (Shape Estimation Algorithm based on BST and Extraction of Directly scattered waves) imaging algorithm is deployed in the application of UWB pulse radar in fields that require real-time operations. However, since the SEABED algorithm uses signals received at multiple locations, this method either needs to scan antennas or to install many antennas. Such systems are inevitably costly and unrealistic for applications such as surveillance. To overcome this problem, a revised SEABED algorithm that estimates unknown target shape based on target motion using only a pair of fixed antennas was developed. However, the method cannot be used when the target moves arbitrarily because it assumes the target motion is parallel to the baseline of the pair of antennas. In this paper, we propose a new UWB radar imaging algorithm that is applicable even for targets with arbitrary motion. The proposed method introduces another antenna which is added to the pair of antennas used in the revised SEABED, and estimates unknown target motion based on the target surface using the three antennas. Next, the proposed method applies the SEABED imaging algorithm to the estimated motion and obtains the target image. Some numerical simulations establishes that the proposed method can accurately estimate the target shape even under severe conditions.

Ultrawide-band pulse radars have immeasurable potential for a high-range-resolution imaging in the near field and can be used for noncontact measurement of industrial products with specular or precision surfaces, such as reflector antenna or aircraft fuselage, or identifying and locating the human body in security systems. In our previous work, we developed a stable and high-speed 3-D imaging algorithm, Envelope, which is based on the principle that a target boundary can be expressed as inner or outer envelopes of spheres, which are determined using antenna location and observed ranges. Although Envelope produces a high-resolution image for a simple shape target that may include edges, it requires an exact connection for observed ranges to maintain the imaging quality. For complex shapes or multiple targets, this connection becomes a difficult task because each antenna receives multiple echoes from many scattering points on the target surface. This paper proposes a novel imaging algorithm without range point connection to accomplish high-quality and flexible 3-D imaging for various target shapes. The algorithm uses an accurate estimation for the direction of arrival using signal amplitudes and realizes direct mapping from observed ranges to target points. Several comparative studies of conventional algorithms clarify that our proposed method accomplishes accurate and reliable 3-D imaging even for complex or multiple boundaries.

Through-the-wall imaging techniques with ultrawideband (UWB) radars are promising candidates for non-destructive testing and reliable human detection, especially in disaster areas, where victims are buried under collapsed walls. These applications require high-resolution target imaging to identify the object shape, such as a human body. We have already proposed a high-quality 3-dimensional (3-D) imaging algorithm in the form of envelope that is aimed at near field sensing for non-contact measurement or target identification for robots. Envelope achieves real-time accurate 3-D imaging with group mapping from multiple observed ranges to target points, and offers a reliable image even in noisy situations. However, this method does not maintain its quality for through-the-wall imaging because an observed range shift due to wall penetration causes a serious distortion in the image. This paper presents a high-resolution 3-D imaging algorithm by modifying the original envelope, and which gives a more accurate object shape behind a wall. Furthermore, to enhance the resolution of the estimated images, this method is combined with a direct waveform compensation method, known as spectrum offset correction. Numerical simulations and an experiment verify that our proposed method achieves high-resolution 3-D imaging for through-the-wall radar applications.

The ultrawideband (UWB) radar is a promising high-resolution 3-D imaging technique for near targets. We have developed a high-speed imaging algorithm, SEABED, for a UWB pulse radar, a key real-time imaging technology. When the algorithm is applied to UWB, antenna scanning for data acquisition takes significantly longer than calculating the SEABED algorithm itself. This presents a serious problem for the real-time application of UWB radar. In this paper, we use pseudonoise (PN) sequences as the transmitting waveforms, while the original work on the SEABED algorithm assumed impulsive short-wave pulses. Using PN sequences enables us to simultaneously transmit signals with multiple antennas, eliminating the need to scan antennas. We demonstrate that the proposed radar system works well using random sequences to suppress direct waves, which is critical in achieving high speeds for imaging.

Ultra-wide band (UWB) radar is capable of high range resolution near fleld sensing, and is thus applicable to security systems designed to identify a human body even when invisible. Although Synthetic Aperture Radar (SAR) creates a stable and accurate target image for such applications, it often sufiers from increased shadow regions in the case of complex or multiple targets. On the contrary, a multiple scattered wave has the potential to enlarge a visible range on target surfaces because it propagates a path which difiers from that of a single scattered wave. While various algorithms based on time-reversal processing with multiple scattered waves have been developed, these require a priori information of the surroundings or a target model. This paper proposes a shadow region imaging algorithm based on the aperture synthesis of multiple scattered waves. While the proposed algorithm only synthesizes a double scattered wave according to its propagation path, it can directly increase the visible area and is applicable to arbitrary target shapes. The results from the numerical simulation and experiment verify that the proposed algorithm directly makes a shadow region visible without a preliminary observation. 1. INTRODUCTION UWB pulse radar is promising as a near fleld sensing technique with high range resolution, and is applicable to non-contact measurement of re∞ector antennae or aircraft bodies that have precise and specular surfaces. It is also applicable to a collision avoidance for automobile under the low visibility. For such applications, various imaging algorithms have been published, as the SEABED accomplishes real-time imaging by using a reversible transform BST (1), and the Envelope+SOC reconstructs the target surface to an accuracy on the order of 1/100 wavelength (2). The SAR algorithm remains promising, in providing a stable and accurate image by using the full information of the received signals (3). However, in the case of complex or multiple targets, any of the algorithms sufiers from increased shadow regions because they use only the single scattered wave for imaging. On the contrary, except for an edge difiraction wave, a multiple scattered wave passes through a path, difierent from that of a single scattered wave, which means that the multiple scattered echo has the potential to enhance the visible range. Although the time reversal algorithms with multiple scattered waves have been proposed when focusing on reliable target detection or accurate positioning in cluttered situations (4{6), they require target modeling or a priori information of the surrounding environment like the walls. To expand the applicability of these methods, this paper proposes a non-parametric imaging algorithm based on the aperture synthesis of multiple scattered signals. The results obtained from numerical simulations and an experiment verify the efiectiveness of the proposed method which is applicable to arbitrary target shapes, and directly enlarges the visible range of the target surface. 2. CONVENTIONAL ALGORITHM Figure 1 shows the system model. It assumes that the target has an arbitrary shape with a clear boundary, and high conductivity like metallic objects. The propagation speed of the radio wave c is assumed to be a known constant. A mono-cycle pulse is used as the transmitting current, and the space is normalized by ‚ as the center wavelength of the pulse. The omni-directional antenna is scanned in the plane z = 0. The real space in which the target and antenna are located is expressed by the parameter r = (x;y;z). z > 0 is assumed for simplicity. s(X;Y;Z) is deflned as the output of the Wiener fllter at the antenna location at (x;y;z) = (X;Y;0), where Z = ct=(2‚) is expressed as a function of the time t. As a spatial measurement in the near fleld, the SAR algorithm has an ability to create a stable and accurate image by using the UWB signal. The distribution image I1(r) obtained by the SAR is formulated as

Ultrawideband pulse radars have a great potential for high-range resolution in near field imaging and can be used for noncontact measuring in precision or specular products such as reflector antennas and aircraft fuselages. We have already proposed a high-speed 3-D imaging algorithm, SEABED, which is based on a reversible transform, which is the boundary scattering transform, between the received signals and the target shape. However, the estimated image with SEABED is unstable with random noise because it utilizes a derivative of the received data. In this paper, we propose a robust 3-D imaging algorithm with an envelope of spheres that completely resolves the instability due to derivative operations. Moreover, to enhance the resolution of estimated images, this method is combined with a direct waveform compensation method that does not sacrifice high-speed calculation. Numerical simulations and an experiment confirm that the proposed method can realize fast, robust, and high-resolution 3-D imaging for arbitrary targets.

SUMMARY UWB (Ultra Wide-Band) pulse radar is a promising candidate for surveillance systems designed to prevent crimes and terror-related activities. The high-speed SEABED (Shape Estimation Algorithm based on BST and Extraction of Directly scattered waves) imaging algorithm, is used in the application of UWB pulse radar in fields that require realtime operations. The SEABED algorithm assumes that omni-directional antennas are scanned to observe the scattered electric field in each location. However, for surveillance systems, antenna scanning is impractical because it restricts the setting places of the devices. In this paper, movement of a body is used to replace antenna scanning. The instantaneous velocity of any given motion is an unknown variable that changes as a function of time. A pair of antennas is used to analyze delay time to estimate the unknown motion. We propose a new algorithm to estimate the shape of a human body using data obtained from a human body passing stationary antennas.

Pulse radars with UWB signals are promising as a high-resolution imaging technique that can be used for the non-destructive measurement of surface details in industrial products such as antennas and aircraft. We have already proposed a fast 3-D imaging algorithm, SEABED, that utilizes a reversible transform between the time delay and the target boundary. However, data acquisition is time-consuming when obtaining an accurate image because it assumes a mono-static radar with 2-D scanning of an antenna. In this paper, we utilize linear array antennas and propose a fast and accurate imaging algorithm. We extend the reversible transform for mono-static radars to apply to bi-static radars to reduce the data acquisition time. The effectiveness of the proposed method is verified with numerical simulations and experiments.

SUMMARY Orbit estimation of space debris, objects of no inherent value orbiting the earth, is a task that is important for avoiding collisions with spacecraft. The Kamisaibara Spaceguard Center radar system was built in 2004 as the first radar facility in Japan devoted to the observation of space debris. In order to detect the smaller debris, coherent integration is effective in improving SNR (Signal-to-Noise Ratio). However, it is difficult to apply coherent integration to real data because the motions of the targets are unknown. An effective algorithm is proposed for echo detection and orbit estimation of the faint echoes from space debris. The characteristics of the evaluation function are utilized by the algorithm. Experiments show the proposed algorithm improves SNR by 8.32 dB and enables estimation of orbital parameters accurately to allow for re-tracking with a single radar.

UWB (Ultra Wide-Band) radar has a variety of applications including security surveillance systems. The SEABED algorithm is a fast imaging method for UWB radar, that uses a reversible transform between the real and data spaces (1). We introduce an intermediate space between the real and data spaces (2). Curves in the intermediate space can be smooth, and can be used to extract quasi-wavefronts (the equi-phase surface). In this paper, we use LMS (Least-Mean-Square) fllters in the intermediate space for imaging arbitrary target shapes.