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Capable of remote presentations of human heart activities, the Doppler cardiogram (DCG) can be detected by an agile Doppler radar sensor. However, as a recently developed technology, its performance needs sustained improvements for the future practical applications. In this work, a continuous-wave, ${V}$ -band Doppler radar sensor was implemented to improve the discrimination of the receiver, for detecting micrometer-level details of heartbeat motions conducted to the chest skin. Besides, new algorithms were derived to achieve high linearity, to demodulate the Doppler phases and retrieve the fine details of DCG waveforms. Experimental measurements to different subjects showed significantly enhanced robustness and accuracy. The obtained results imply a promising potential of DCGs in wide application scenarios such as medical wisdom and personal healthcare. [ABSTRACT FROM AUTHOR]

In the past decades, continuous Doppler radar sensor-based biosignal detections have attracted many research interests. A typical example is the Doppler heartbeat detection. While significant progresses have been achieved, reliable, time-domain accurate demodulation of biosignals in the presence of unavoidable direct current (dc) offsets remains a technical challenge. Aiming to overcome this difficulty, we propose in this article a novel demodulation algorithm that does not need to trace and eliminate dynamic dc offsets based on approximating segmented arcs in a quadrature constellation of sampling data to directional chords. Assisted by the principal component analysis, such chords and their directions can be deterministically determined. Simulations and experimental validations showed full recovery of micrometer-level pendulum movements and strongly noised human heartbeats, verifying the effectiveness and accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

Personal devices such as smartphones and tablets are rapidly becoming personal communication, information, and control centers. Apart from multitouch screens, human gestures are considered as a new interactive human–smart device interface. In this work, we propose a noncontact solution to implement hand gesture recognitions for smart devices. It is based on a continuous wave, time-division-multiplexing (TDM), single-input multiple-output (SIMO) Doppler radar sensor that can be realized by slightly modifying existing RF front ends of smart devices, and a machine-learning algorithm to recognize predefined gestures by classifying deterministic Doppler signals. An experimental setup emulating a smartphone-based radar sensor was implemented, and the experimental results verified the robustness and the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

To date, 3-D radar imaging of moving objects in a multipath coordinate space close to antennas remains a technical challenge. In this work, we demonstrate the implementation of such 3-D motion imaging based on a continuous-wave, multiple-input multiple-output (MIMO) Doppler radar sensor (DRS). Working with a time-division-multiplex (TDM) scheme, this portable, cost-effective DRS is a bistatic system with one transmitting and one receiving channels, and a radio frequency (RF) switch in each channel toggling between two antennas. Assisted by algorithms specifically derived for the TDM-MIMO architecture, highly sensitive 3-D imaging performed in a multipath indoor environment was experimentally implemented. Numerical simulations and experimental demonstrations verified the effectiveness and accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

Noncontact detection of human vital signs based on miniaturized Doppler radar systems (DRSs) can be widely used in healthcare and biomedical applications. Although significant progresses have been achieved, a reliable wireless vital signs detection in the presence of large-scale random human body movements remains a technical challenge. In this paper, based on a comprehensive use of the high-dynamic-range radar architecture and linearized Doppler phase demodulation algorithms, we further introduce matched filters to retrieve the respiration and heartbeat spectra completely concealed under the wideband noise floor caused by large-scale body movements. Along with an existing 5.8-GHz system, a single-board integrated DRS operating at 24 GHz was designed to verify the effectiveness of the used architecture and algorithms. Experimental results comply with the theoretical expectation. The obtained results imply the potential to implement practical bioradar systems for human noncontact vital signs detections. [ABSTRACT FROM AUTHOR]

This paper reviews recent progress of portable short-range noncontact microwave radar systems for motion detection, positioning, and imaging applications. With the continuous advancements of modern semiconductor technologies and embedded computing, many functionalities that could only be achieved by bulky radar systems in the past are now integrated into portable devices with integrated circuit chips and printed circuits boards. These portable solutions are able to provide high motion detection sensitivity, excellent signal-to-noise ratio, and satisfactory range detection capability. Assisted by on-board signal processing algorithms, they can play important roles in various areas, such as health and elderly care, veterinary monitoring, human-computer interaction, structural monitoring, indoor tracking, and wind engineering. This paper reviews some system architectures and practical implementations for typical wireless sensing applications. It also discusses potential future developments for the next-generation portable smart radar systems. [ABSTRACT FROM AUTHOR]

This paper presents a portable radar system for short-range localization, inverse synthetic aperture radar imaging, and vital sign tracking. The proposed sensor incorporates frequency-modulated continuous-wave (FMCW) and interferometry (Doppler) modes, which enable this radar system to obtain both absolute range information and tiny vital signs (i.e., respiration and heartbeat) of human targets. These two different operation modes can be switched through an on-board microcontroller. To simplify the system, the proposed radar utilizes the audio card of a laptop to sample the baseband signal. The FMCW mode of the radar uses operational-amplifier-based circuits to generate an analog sawtooth signal and a reference pulse sequence (RPS). The RPS is locked to the sawtooth signal to obtain coherence for the radar system. For the interferometry mode, a low-intermediate-frequency modulation method is implemented to avoid the slow vital signs from being distorted by the high-pass filter of the audio card. Several experiments were carried out to reveal the capability and distinct operational features of the proposed portable hybrid radar. The experiments also showed that the system can easily detect glass, which is usually difficult to identify for optical-based sensors. In addition, 2-D scanning in a complex environment revealed that the proposed radar was able to differentiate human targets from other objects. Moreover, ISAR images were used to isolate moving human targets from surrounding clutter. Finally, the proposed radar also demonstrated its ability to accurately measure vital signs when a human subject sits still. [ABSTRACT FROM PUBLISHER]

Short-range continuous-wave Doppler radar sensors have been mainly used for noncontact detection of various motions. In this paper, we investigate the feasibility to implement the function of a remote mouse, an input device of a computer, by recognizing human gestures based on a dual-channel Doppler radar sensor. Direct conversion architecture, symmetric subcarrier modulation, and bandpass sampling techniques are used to obtain a cost-effective solution. An arcsine algorithm and a motion imaging algorithm are proposed to linearly reconstruct the hand and finger motions in a 2-D plane from the demodulated Doppler phase shifts. Experimental results verified the effectiveness of the proposed architecture and algorithms. Different from the frequency-domain “micro-Doppler” approach, the proposed remote gesture recognition based on linear motion reconstruction is able to recognize definitive signatures for the corresponding motions, exhibiting promising potential in practical applications of human–computer interaction. [ABSTRACT FROM PUBLISHER]

Recently, progresses have been made in hand gesture recognition based on Doppler radar sensors. However, it remains a technical challenge to avoid the interfering human motions. An example is detecting hand gestures in the presence of random body movements. In this paper, we propose a solution based on a Single-input Multiple-output frontend and a blind motion separation algorithm. Assisted by an additional receiving channel, Doppler signals caused by different motions can be separated by extending the algorithm originally developed for separating human voices. The experimental separation of hand gestures from interfering movements verified the effectiveness of this approach. The proposed solution can be potentially used in novel applications such as human gait and gesture recognitions. [ABSTRACT FROM AUTHOR]

Utilizing microwave continuous-wave Doppler radars to wirelessly detect mechanical vibrations have been attracting more and more interests in recent years. In this paper, aiming to solve the null point and nonlinear issues in small-angle approximation-based Doppler radar sensors and eliminate the codomain restriction in the arctangent demodulation approach, we propose and investigate an extended differentiate and cross-multiply (DACM) algorithm. With an additional accumulator, the noise performance of the original DACM algorithm is improved. Moreover, the amplitude information of the vibration can be directly retrieved from accumulation without involving any distance-dependent issue. Experimental validations show that the proposed algorithm can fully recover the vibration patterns with the measured noncalibrated amplitude agreeing well with the known precalibrated data. Application examples of mechanical fault detection and human vital sign detection are demonstrated, showing a wide range of potential applications of this algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents an experimental investigation that demonstrates the possibility of 1-D imaging of human cardiac motion using a microwave Doppler sensor. Compared with the previous works that primarily monitored human respiration and heartbeat rates, the reconstruction of cardiac motion in this work will provide more information for time-domain clinical diagnosis. To fully recover the motion information from the backscattered microwave signal, an instrument-based digital-IF Doppler radar sensor employing a dc-offset removal and an extended differentiate and cross-multiply algorithm was used. A series of experiments were performed to investigate the effectiveness of the cardiac imaging from different orientations of a subject. Analysis on the experimental results indicates that in addition to the respiration and heartbeat rates, a 1-D time-domain cardiac motion that fits well with the known physiological description can be obtained. Our work reveals that substantial cardiac activity information is carried by the Doppler shifts of the backscattered microwave reflected from a human chest. The information can be reconstructed by properly designed hardware and algorithms. The possibility of noncontact cardiac imaging would have a great potential in clinical diagnosis and treatment of human heart diseases. [ABSTRACT FROM PUBLISHER]

Doppler radar has been used for small-scale displacement detection, e.g. respiration and heartbeat. The motion amplitude is less than half a carrier wavelength. In this letter, a novel Doppler radar technique is presented for accurate tracking of large-scale displacement of several carrier wavelengths, e.g., the water level variation. The DC-coupled architecture is used to ensure precise measurement of slow motions even with stationary moment. A novel signal processing approach is proposed to increase the demodulation linearity to deal with inconstant signal amplitude, dynamic dc offset and phase ambiguity in large-scale displacement tracking. Experiments have been carried out in the outdoor environment to monitor the relative water level position in a rain barrel when the water was pumped in or drained out. It is shown that the proposed technique can accurately gauge the relative water level variation of 3.5 wavelengths with mm accuracy. [ABSTRACT FROM PUBLISHER]