High-Order Signal Processing for Ultra-Wideband Radar Signals

Ultra wideband (UWB) technology has been widely researched for both radar and communication systems. However, detecting the presence of a signal is essential and challenging for many applications. In this paper, we propose a novel detector for UWB radar signals with UWB communication interference by exploiting the signal cyclic features. A single-cycle detector based on fundamental cyclic frequency of radar signal is proposed to detect UWB radar signal. Simulation results illustrate the possibility to detect the UWB radar signal under the coexistence of a UWB communication signal and the proposed scheme is compared with conventional detectors for UWB radar systems.

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.

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.

The propagation of ultra wide band (UWB) signals is analyzed. The UWB radar output signals are formed both with transmitter and antenna. Moreover, they can be substantially affected due to electromagnetic wave propagation through walls and multipath effects. Multipath effects are analyzed and simulated numerically for various cases with several heights and distances.

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.

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.

Ultrawideband (UWB) radar technology has emerged as one of the preferred choices for through-wall detection due to its high range resolution and good penetration. The resolution is a result of high bandwidth of UWB radar and helpful for better separation of multiple targets in complex environment. Detection of human targets through a wall is interesting in many applications. One significant characteristic of human is the periodic motion, such as breathing and limb movement. In this letter, we apply the UWB radar system in through-wall human detection and present the methods based on fast Fourier transform and S transform to detect and identify the human's life characteristic. In particular, we can extract the center frequencies of life signals and locate the position of human targets from experimental data with high accuracy. Compared with other research studies in through-wall detection, this letter is concentrated in the processing and identifying of the life signal under strong clutter. It has a high signal-to-noise ratio and simpler to implement in complex environment detection. We can use the method to search and locate the survivor trapped under the building debris during earthquake, explosion, or fire.

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.

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.

Abstract. For indoor positioning, ultra-wideband (UWB) radar comes to the forefront due to its strong penetration, anti-jamming, and high-precision ranging abilities. However, due to the complex indoor environment and disorder of obstacles, the problems of diffraction, penetration, and ranging instability caused by UWB radar signals also emerge, which make it difficult to predict the noise and leads to a great impact on the accuracy and stability of the measurement data in the short term. Therefore, the abnormal value migration of the positioning trajectory occurred in real-time positioning. To eliminate this phenomenon and provide more accurate results, the abnormal values need to be removed. It is not difficult to eliminate abnormal values accurately based on a large number of data, but it is still a difficult problem to ensure the stability of the positioning system by using a small number of measurement data in a short time to eliminate abnormal value in real-time ranging data. Thus, this paper focuses on the experimental analysis of a UWB-based indoor positioning system. By repeatedly measuring the range , a large number of measurement data can be obtained. Using the massive data to train linear regression models, we get the parameter of the linear model of range data measured with the UWB radar. Based on the Gaussian function outlier detection, abnormal values are eliminated, and putting the new range data into the regression model trained by us, the ranging error is reduced by nearly 50 % compared with the peak and mean ranging errors in general.

In this paper, we provide a new approach in ultra Wideband (UWB) radars based on a Fourier series with time-varying coefficients resulting in a second-order cyclostationary characteristics for stationary (low-mobility) target and almost second-order cyclostationary characteristics in moving target scenario. We also provide a new method for blind detection based on detecting the presence of cyclostationarity. The new MMSE estimator is based on the unconditional orthonormal signal representation for almost cyclostationary characteristics, moreover, based on this estimation, a new method for detection of UWB radar signals in presence of (non)-Gaussian additive noise in stationary and moving target scenario is provided. We utilize the discrete prolate spheroidal sequences (DPSS) as the unconditional orthonormal signals. The purposed methods require no a priori information, except the pulse repetition interval, to detect signal of interest. Simulations for CFAR detectors are presented to compare the introduced new method with cyclostationary detector and the conventional UWB radar detectors called interleaved periodic correlation processing (IPCP).

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.

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.