Through-Wall Detection of Human Being's Movement by UWB Radar

Experimental study of through-wall human detection using ultra wideband radar sensors

The safety aspects of people exposed to the field emitted by ultra wideband (UWB) radar, operating both in the spatial environment and on ground, for breath activity monitoring are analyzed. The basic restrictions and reference levels reported in the ICNIRP safety guideline are considered, and the compliance of electromagnetic fields radiated by a UWB radar with these limits is evaluated. First, simplified analytical approaches are used; then, both a 3-dimensional multilayered body model and an anatomical model of the head have been used to better evaluate the electromagnetic absorption when a UWB antenna is placed in front of the head. The obtained results show that if the field emitted by the UWB radar is compliant with spatial and/or ground emission masks, then both reference levels and basic restrictions are largely satisfied.

Heart-beat detection has found many applications in military and bio-medical areas. Many of these applications focus on the use of impulse based ultra wide band (UWB) radars. This in-turn requires expensive hardware and consumes more power (for a given range) compared to stepped frequency continuous wave (SFCW) radars. However, the micro-Doppler characteristic of human body can be detected by SFCW radar at much lower sampling rates and power. It can help in distinguishing a living and a non-living target. In addition, SFCW radar can help separate Doppler signatures in down-range, thus enabling it to look for multiple human targets. UWB radars operate at a bandwidth higher than 0.5 GHz and have the benefit of high range resolution. This paper focuses on the experimentation of a technique to obtain the range and Doppler of a human body using UWB-SFCW radar and moving target indicator (MTI) filter, in the presence of an interfering wall.

Ultra-wideband (UWB) radar and impulse radar can provide new opportunities for information recovery from radar return signal analysis. If the UWB radar echo signal processing task is converted into a “black-box” system identification problem, then high order signal processing can offer increased performance in terms of characterizing radar returns for discrimination between targets/materials. The range supported the generation, transmission, and collection of the UWB signals covering the 300 to 900 MHz UHF band. The available information content of UWB radar returns can extend the potential capabilities of radar beyond range and Doppler measurements to target characterization and classification tasks. Preliminary results suggest that UWB radar responses from different materials exhibit different, and characteristic, bispectral and/or kernel signatures, which can be used for enhanced detection and classification under low signal- to-noise ratio conditions. Radar target identification from the return signal characteristics has been a radar design objective for years.

To date, ultra-wideband (UWB) radar is one of the leading technologies applied in the field of non-contact vital sign monitoring. A number of studies have focused on processing reflected UWB pulse signals into breathing and heart activities; however, most have emphasised the use of stationary subjects in their data collection processes. Therefore, this paper presents a feasible study conducted to extract the human vital signs of a non-stationary subject during sleep and the optimum position of the UWB radar. The proposed algorithm could measure the respiration rate (RR) and heart rate (HR) recorded regardless of any random body movements during sleep. An analysis of the entire slow time region for the signal was performed to remove random movement signals from the subject by implementing a sinusoidal fitting algorithm to monitor the periodic movement of the chest wall. Next, the value of R-squared was used to find the fit between the algorithm and output signals, and then, the signal was transformed into a frequency domain via Fourier transform (FT). This allowed the determination of the dominant peak from the breathing and heart rates before changing to the rate per minute. An experiment was also done to monitor three different UWB radar positions (i.e. top, side, and bottom of the bed) to identify its optimum location during sleep. All results were then compared with the polysomnography signal. The result found that the top position has the lowest error rate percentage for both RR and HR with only 0.72% and 3.71%, respectively.

Ultra wideband (UWB) radar has been extensively investigated both theoretically and practically for the identification buried artifacts. Ground probe radar (GPR) concentrates on the identification of lightly buried land mines, unexploded ordnance (UXO) and archeological targets. The same technology is proposed in a similar context for the rapid identification of in vivo implanted metallic prostheses. The technique is based on resonance based target identification and the paper investigates UWB scattering from a metallic hip prosthesis in free space as a first step in the identification process.

The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.

In support of the Army vision for increased mobility, survivability, and lethality, we are investigating the use of ultra-wideband (UWB) radar technology to enhance unmanned ground vehicle missions. The ability of UWB radar technology to detect foliage-concealed objects could provide an important obstacle avoidance capability for robotic vehicles, which would improve the speed and maneuverability of these vehicles and consequently increase the survivability of the U.S. forces. This technology would address the particular challenges that confront robotic vehicles such as large rocks hidden in tall grass and voids such as ditches and bodies of water. This paper describes electromagnetic model predictions of the radar cross section of potential robotic vehicle obstacles. These model predictions will be used to guide the data collection scenarios for the Army Research Laboratory (ARL) ultra-wideband BoomSAR system. Using a combination of the models, simulations, and BoomSAR data we investigate the operating parameters (imaging angles, frequencies, bandwidth, etc.) for an obstacle-avoidance UWB radar.

Ultra-wideband (UWB) radar has become increasingly popular for short-range sensing applications due to the commercial availability, energy efficiency, and integration potential within existing wireless ecosystems. The widespread availability of commercial UWB chips, originally designed for secure ranging and localisation, has made radar sensing more accessible. These chips are low-cost, energy-efficient, and already integrated into many consumer devices, making them an attractive platform for developing radar-based sensing solutions. The high rate pulse repetition frequency (HRP) UWB PHY uses short pulses across a wide frequency range and allows for precise time-of-flight measurements and fine spatial resolution. This makes it well-suited for detecting small movements and resolving targets in cluttered environments. Sensing applications range from indoor localisation and gesture recognition to monitoring of vital signs. However, evaluating the sensing performance of UWB radar systems in diverse scenarios remains challenging. The complexity of real-world environments and the variability of target behaviours make it difficult to test UWB devices consistently. This paper presents a method for simulating UWB radar targets and assessing sensing performance under controlled conditions. By emulating target reflections and clutter characteristics, the proposed approach enables repeatable and scalable testing of UWB radar systems, facilitating the development and benchmarking of robust sensing solutions.

This paper presents a novel technique to recover the missing spectral information due to RF spectral restriction in ultra-wideband (UWB) synthetic aperture radar (SAR) imaging. We address a critical problem in UWB radar imaging: radar transmission is prohibited in reserved frequency bands specified by local frequency management agencies. We model the problem in a compressed sensing setup where a time-sparse received radar signal at each aperture is collected in the incoherent frequency domain using a stepped-frequency implementation. Using electromagnetic (EM) finite-difference, time-domain (FDTD) data of various targets and clutter objects computed at all viewing aspect angles, we show that the proposed technique can successfully recover the relevant information on targets of interest even from a large percentage of missing frequency bands.

There is a great need for new technology that helps ensure the well-being of senior citizens who have compromised health and are at an elevated risk of injury due to falls. Being able to detect posture and postural changes may be helpful in prediction and prevention of impending falls. Ultra-Wideband (UWB) radar is an attractive means for patient monitoring because it is inexpensive, capable of penetrating obstacles, privacy preserving and it consumes little power. In this paper, classification of postures, namely sitting, standing and lying is presented using stand-off sensing using UWB radar in an indoor environment. It is found that using location specific classifiers, overall accuracy can be improved. In this paper, a decision tree classifier capable of achieving 85% overall accuracy is proposed. This classifier uses 33 features from 10 second data sample segments.

Recognizing activities of daily living from UWB radars and deep learning

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) radars belong to the most suitable technologies for short-range detection and tracking of multiple moving persons in critical situations and environments. The results of applications of a single UWB radar for such scenarios have shown that the target located nearby the radar antennas is very often visible only. The other targets can be also detected but with less reliability than that of the target located the most closely to the radar antennas. This is the crucial problem of UWB radar applications for short-range detection and tracking of multiple moving persons. In this paper, we will outline the origin of this effect as the impact of the mutual shadowing of targets within multiple person tracking scenario. This explanation will be verified by the experimental analyses.

Due to the recent advances in ultrawideband (UWB) radar technologies, there has been widespread interest in the medical applications of this technology. We propose the multimodal combination of magnetic resonance (MR) and UWB radar for improved functional diagnosis and imaging. A demonstrator was established to prove the feasibility of the simultaneous acquisition of physiological events by magnetic resonance imaging and UWB radar. Furthermore, first in vivo experiments have been carried out, utilizing this new approach. Correlating the reconstructed UWB signals with physiological signatures acquired by simultaneous MR measurements, representing respiratory and myocardial displacements, gave encouraging results which can be improved by optimization of the MR data acquisition technique or the use of UWB antenna arrays to localize the motion in a focused area.