Range-Point Migration-Based Image Expansion Method Exploiting Fully Polarimetric Data for UWB Short-Range Radar

Ultrawideband radar with high-range resolution is a promising technology for use in short-range 3-D imaging applications, in which optical cameras are not applicable. One of the most efficient 3-D imaging methods is the range-point migration (RPM) method, which has a definite advantage for the synthetic aperture radar approach in terms of computational burden, high accuracy, and high spatial resolution. However, if an insufficient aperture size or angle is provided, these kinds of methods cannot reconstruct the whole target structure due to the absence of reflection signals from large part of target surface. To expand the 3-D image obtained by RPM, this paper proposes an image expansion method by incorporating the RPM feature and fully polarimetric data-based machine learning approach. Following ellipsoid-based scattering analysis and learning with a neural network, this method expresses the target image as an aggregation of parts of ellipsoids, which significantly expands the original image by the RPM method without sacrificing the reconstruction accuracy. The results of numerical simulation based on 3-D finite-difference time-domain analysis verify the effectiveness of our proposed method, in terms of image-expansion criteria

Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods

Parametric Wind Velocity Vector Estimation Method for Single Doppler LIDAR Model

Doppler lidar (LIght Detection And Ranging) can provide accurate wind velocity vector estimates by processing the time delay and Doppler spectrum of received signals. This system is essential for real-time wind monitoring to assist aircraft taking off and landing. Considering the difficulty of calibration and cost, a single Doppler lidar model is more attractive and practical than a multiple lidar model. In general, it is impossible to estimate two or three dimensional wind vectors from a single lidar model without any prior information, because lidar directly observes only a 1-dimensional (radial direction) velocity component of wind. Although the conventional VAD (Velocity Azimuth Display) and VVP (Velocity Volume Processing) methods have been developed for single lidar model, both of them are inaccurate in the presence of local air turbulence. This paper proposes an accurate wind velocity estimation method based on a parametric approach using typical turbulence models such as tornado, micro-burst and gust front. The results from numerical simulation demonstrate that the proposed method remarkably enhances the accuracy for wind velocity estimation in the assumed modeled turbulence cases, compared with that obtained by the VAD or other conventional method

Three-Dimensional Imaging Method Incorporating Range Points Migration and Doppler Velocity Estimation for UWB Millimeter-Wave Radar

High-resolution, short-range sensors that can be applied in optically challenging environments (e.g., in the presence of clouds, fog, and/or dark smog) are in high demand. Ultrawideband (UWB) millimeter-wave radars are one of the most promising devices for the above-mentioned applications. For target recognition using sensors, it is necessary to convert observational data into full 3-D images with both time efficiency and high accuracy. For such conversion algorithm, we have already proposed the range points migration (RPM) method. However, in the existence of multiple separated objects, this method suffers from inaccuracy and high computational cost due to dealing with many observed RPs. To address this issue, this letter introduces Doppler-based RPs clustering into the RPM method. The results from numerical simulations, assuming 140-GHz band millimeter radars, show that the addition of Doppler velocity into the RPM method results in more accurate 3-D images with reducing computational costs

Noncontact Monitoring of Vital Signs with RGB and Infrared Camera and Its Application to Screening of Potential Infection

In recent years, much attention is being paid to research and development of technology that provides noncontact measurement of vital signs, i.e., heart rate, respiration, and body temperature, which are important for understanding the state of a person’s health. As technology for sensing biological information has progressed, new biological measurement sensors have been developed successively. There have also been reports regarding methods for measuring respiration or heart rate using pressure sensors, microwave radar, air mattresses, or high-polymer piezoelectric film. The methods have wide-ranging applications, including systems for monitoring of elderly people, identification of sleep apnea, detection of patients suspected to have an infectious disease, and noncontact measurement of stress levels. In this chapter, the principles behind noncontact measurement of respiration and heartbeat using infrared/RGB facial-image analysis are discussed, along with the applications for such measurement in the detection of patients suspected to be suffering from infectious diseases

Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation

Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis

Nonparametric UWB Radar Imaging Algorithm for Moving Target Using Multi-static RPM Approach

Abstract—Ultra-wideband (UWB) pulse radar is promising technology for the imaging sensors of rescue robots operating in disaster scenarios, where optical sensors are not applicable be-cause of dusty air or strong backlighting. An UWB radar imaging algorithm for a target with arbitrary motion has already been proposed. That algorithm is based on a circular approximation of the target boundary, and employs measured distances. Although it obtains an accurate target motion and image with few antennas for some targets, it is hardly applicable to complex target shapes, particularly if targets have specular surfaces or surface edges. As a substantial solution to this problem, this paper proposes an original imaging algorithm for a moving target with arbitrary shape, which is based on normal vector matching on the target surface with multi-static observations. Specifically, this study extends the existing RPM (Range Points Migration) algorithm, which is a super-high-resolution and accurate imaging approach, to the multi-static radar model. Numerical simulations show that our proposed algorithm accomplishes extremely accurate target surface extraction, and motion estimation at the order of 1/100 wavelength, even for a noisy environment. Key words—UWB pulse radar, Moving target, Range points migration, Shape estimation