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During the global epidemic, non-contact methods for monitoring the vital signs of several people have become particularly important. Advanced signal processing techniques have recently been demonstrated to separate and track the v...
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During the global epidemic, non-contact methods for monitoring the vital signs of several people have become particularly important. Advanced signal processing techniques have recently been demonstrated to separate and track the vital signs of multiple people. In this paper, we further develop the multi-person vital signs identification (VSign-ID) system to make non-contact detection available in public places. VSign-ID not only extracts multi-person vital signs but also states from whom these vital signs are collected. We utilize multiple doppler radars to expand the effective range of the measurement area and propose a space and time matching mechanism for vital signs identification. We use a thermal camera to detect the number of people and their movements. VSign-ID efficiently coordinates these two types of sensors (i.e., the doppler radars and the thermal camera) to track and identify the respiration rates and heartbeat rates of multiple people. A series of experiments and simulations are conducted to measure the efficiency of VSign-ID. In the case of five people sitting closely, the estimation errors for respiration and heartbeat rates are −4.85 dB and −2.36 dB lower than the standard resolution of the system, respectively, despite using only two independent radars.
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Accurate motion estimation is a challenging problem for agile radar platforms, even when state-of-the-art inertial navigation sensors (INSs) are used. However, it is an important problem to solve as it can have a large impact on t...
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Accurate motion estimation is a challenging problem for agile radar platforms, even when state-of-the-art inertial navigation sensors (INSs) are used. However, it is an important problem to solve as it can have a large impact on the performance of radar modes, such as synthetic aperture radar (SAR). This study addresses the motion estimation problem of agile radar platforms from the perspective of an omnidirectional radar array. In this study, we perform an analysis on the applicability of an omnidirectional radar array to explicitly estimate the motion of an agile SAR platform and improve imaging quality. Building on existing 1-D SAR motion compensation techniques, we develop a method to estimate the 3-D motion of the radar platform utilizing its height and velocity vector. Using a prototype radar developed at the Netherlands Organisation for Applied Scientific Research, we experimentally verify that using the proposed velocity estimation method alone, we achieve comparable positioning performance to that of a state-of-the-art INS, making it possible to perform INS-free SAR imaging by using arbitrary flight paths. We also show that fusing the radar positioning estimates obtained from the proposed methods with the INS output yields an additional increase in SAR imaging performance, improving the resolvability and detectability of weak targets.
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Advanced driver assistance systems (ADASs) and autonomous vehicles rely on different types of sensors, such as camera, radar, ultrasonic, and LiDAR, to sense the surrounding environment. Compared with the other types of sensors, m...
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Advanced driver assistance systems (ADASs) and autonomous vehicles rely on different types of sensors, such as camera, radar, ultrasonic, and LiDAR, to sense the surrounding environment. Compared with the other types of sensors, millimeter-wave automotive radar has advantages in terms of cost and reliability under bad weather conditions (e.g., snow, rain, and fog) and does not suffer from light condition variations (e.g., darkness). Typical radar devices used in today's commercial vehicles with ADAS features produce sparse point clouds in low angular resolution with a limited number of antennas. In this article, we present a machine-learning-aided signal processing chain to suppress the radar imaging blur effect introduced by the phase migration in time-division multiplexing multiple-input multiple-output radar, to generate low-level high-resolution radar bird's-eye view (BEV) spectra with rich object's features. Compared with radar point clouds, there is no information loss in radar BEV spectra. We then propose a temporal-fusion distance-tolerant single-stage object detection network, termed as TDRadarNet, and an enhanced version, TDRadarNet+, to robustly detect vehicles in both long and short ranges on radar BEVs. We introduce a first-of-its-kind multimodel dataset, containing 14 800 frames of high-resolution low-level radar BEV spectra with synchronized stereo camera RGB images and 3-D LiDAR point clouds. Our dataset achieves 0.39-m range resolution and $\text{1.2}^\circ$ degree azimuth angular resolution with 100-m maximum detectable range. Moreover, we create a subdataset, the Doppler Unfolding dataset, containing 244 140 beam vectors extracted from the 3-D radar data cube. With extensive testing and evaluation, we demonstrate that our Doppler unfolding network achieves 93.46% Doppler unfolding accuracy. Compared to YOLOv7, a state-of-the-art image-based object detection network, TDRadarNet, achieves a 70.3% average precision (AP) for vehicle detection, demonstrating a 21.0% improvement; TDRadarNet+ achieves a 73.9% AP, showing a 24.6% improvement in performance.
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The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a gro...
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The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a ground-based S-band polarimetric radar in South China based on a volume-matching method. Good consistency is found for the reflectivity factor (Z) calibration of the two instruments. From the perspective of microphysics, the mass-weighted mean diameter ( $D_{m})$ estimates correspond well with those of the ground-based radar in the inner swath of the normal scan (NS); however, underestimation is found for the raindrop number concentration, indicated by the generalized intercept parameter ( $N_{w})$ , especially for the intense echoes. Thus, the GPM DPR product may fail to depict the microphysical characteristics of small-to-medium raindrops in high concentration for heavy rainfall in South China. This is attributed to the negative Z bias of the DPR caused probably by insufficient correction of attenuation, which also leads to clear underestimation in the liquid water content (W) and the rainfall rate (R) products for intense echoes. In the outer swath where only single-frequency retrieval is available, overestimation in $D_{m}$ exists regardless of echo intensity level, and more underestimation can be found in $N_{w}$ , W, and R especially for intense echoes. In the selected typhoon and squall line cases, better capability in revealing microphysical properties is also found for the inner swath of the NS. After adjusting the scan mode, the performance of the precipitation products in the outer swath can be improved by dual-frequency retrievals in the future.
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Practical and accurate estimation of 3-D wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter/multiple receivers) radar architectures offer a cost-effective solution for obtaining the multiple ...
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Practical and accurate estimation of 3-D wind fields is an ongoing challenge in radar meteorology. Multistatic (single transmitter/multiple receivers) radar architectures offer a cost-effective solution for obtaining the multiple Doppler measurements necessary to achieve such estimates. Existing multistatic weather radars, while less costly than comparable monostatic networks, have been constrained by the need for specialized equipment and software to perform frequency and pulse timing synchronization with the transmitting radar. This article describes the implementation of a passive radar network that performs pulse timing and carrier frequency synchronization through measurements of the sidelobe radiation of the transmitting radar. This makes it possible for the receiver modules to be constructed with minimal size and cost, and it allows for their use in coordination with any in-band transmitter capable of recording time-stamped pointing angle information. A prototype network consisting of two passive receivers has been constructed in the Oklahoma City, OK, USA. Weather observations collected using the radiation from a WSR-88D have been used to validate the accuracy of velocity measurements obtained through this technique.
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We introduce radar polarimetry, which generally is a less widely known concept of radar engineering, and explain the principles and applications of polarimetric Doppler weather radars. For example, a polarimetric radar can disting...
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We introduce radar polarimetry, which generally is a less widely known concept of radar engineering, and explain the principles and applications of polarimetric Doppler weather radars. For example, a polarimetric radar can distinguish among precipitation particles of different shapes, compositions, and orientations. A relatively simple electromagnetic idea resulted in the 2012 upgrade of the National Weather Service (NWS) network of 160 high-resolution Doppler weather radars in the United States, with dual-polarization technology. We describe the details of the Colorado State University (CSU)–CHILL research radar, featuring exceptional polarization purity and dual-frequency operation, and its setup and role in winter field experiments in Colorado.
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The acquisition and utilization of polarization information is an open topic because polarization plays a significant role in many fields, such as radar target detection, classification, recognition, synthetic aperture radar (SAR)...
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The acquisition and utilization of polarization information is an open topic because polarization plays a significant role in many fields, such as radar target detection, classification, recognition, synthetic aperture radar (SAR) imaging, anti-jamming, etc. Inspired by the bat sound waveform and combined with the properties of the stepped linear frequency modulation (SLFM) signal, in this article, a novel polarization waveform, namely, simultaneous SLFM (SSLFM), which can be used for small airborne and spaceborne polarimetric SAR (PolSAR) platform, is proposed. Moreover, a method based on the SSLFM signal is proposed to realize simultaneous polarization measurement and double high-resolution imaging. It is demonstrated that the method can better make use of the polarization information of echoes and obtain a better imaging effect. The feasibility of the proposed method is verified by simulation experiments.
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Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters, the accuracy and confidence of meteorology target detection are reduced. In this paper, a deep convolu...
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Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters, the accuracy and confidence of meteorology target detection are reduced. In this paper, a deep convolutional neural network (DCNN) is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input. For each weather radar image, the corresponding digital elevation model (DEM) image is extracted on basis of the radar antenna scanning parameters and plane position, and is further fed to the network as a supplement for ground clutter suppression. The features of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process. Then the network parameters are updated by the back propagation iteration of the training error. Experimental results on the real measured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors. Meanwhile, the network outputs are in good agreement with the expected meteorology detection results (labels). It is demonstrated that the proposed network would have a promising meteorology observation application with minimal effort on network variables or parameter changes.
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This letter aims to show the potential of using polarimetric parameters to distinguish between large birds and unmanned aerial vehicles (UAVs) of comparable size in the context of a modern long-range air defense radar. Time is a c...
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This letter aims to show the potential of using polarimetric parameters to distinguish between large birds and unmanned aerial vehicles (UAVs) of comparable size in the context of a modern long-range air defense radar. Time is a critical resource in such systems, and techniques for robust noncooperative target recognition not relying on spatial resolution or long dwell times are highly desired. Furthermore, methods less dependent on target micromotion are, in many cases, required. Methods exploiting polarimetric features are shown to have potential in both cases. An experiment in S-band shows that a simple nearest-neighbor classifier can achieve good separation between UAVs and birds both with and without detectable micromotion based on a set of polarimetric parameters alone.
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