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1. Identifying gravity waves launched by the Hunga Tonga–Hunga Ha′apai volcanic eruption in mesosphere/lower-thermosphere winds derived from CONDOR and the Nordic Meteor Radar Cluster

   https://doi.org/10.5194/angeo-41-197-2023  

   Stober, Gunter; Liu, Alan; Kozlovsky, Alexander; Qiao, Zishun; Krochin, Witali; Shi, Guochun; Kero, Johan; Tsutsumi, Masaki; Gulbrandsen, Njål; Nozawa, Satonori; et al (January 2023, Annales Geophysicae)

   Abstract. The Hunga Tonga–Hunga Ha′apai volcano eruption was a unique event that caused many atmospheric phenomena around the globe. In this study, we investigate the atmospheric gravity waves in the mesosphere/lower-thermosphere (MLT) launched by the volcanic explosion in the Pacific, leveraging multistatic meteor radar observations from the Chilean Observation Network De Meteor Radars (CONDOR) and the Nordic Meteor Radar Cluster in Fennoscandia. MLT winds are computed using a recently developed 3DVAR+DIV algorithm. We found eastward- and westward-traveling gravity waves in the CONDOR zonal and meridional wind measurements, which arrived 12 and 48 h after the eruption, and we found one in the Nordic Meteor Radar Cluster that arrived 27.5 h after the volcanic detonation. We obtained observed phase speeds for the eastward great circle path at both locations of about 250 m s−1, and they were 170–150 m s−1 for the opposite propagation direction. The intrinsic phase speed was estimated to be 200–212 m s−1. Furthermore, we identified a potential lamb wave signature in the MLT winds using 5 min resolved 3DVAR+DIV retrievals. 

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2. Atmospheric tomography using the Nordic Meteor Radar Cluster and Chilean Observation Network De Meteor Radars: network details and 3D-Var retrieval

   https://doi.org/10.5194/amt-14-6509-2021  

   Stober, Gunter; Kozlovsky, Alexander; Liu, Alan; Qiao, Zishun; Tsutsumi, Masaki; Hall, Chris; Nozawa, Satonori; Lester, Mark; Belova, Evgenia; Kero, Johan; et al (January 2021, Atmospheric Measurement Techniques)

   Abstract. Ground-based remote sensing of atmospheric parameters is often limited to single station observations by vertical profiles at a certain geographic location. This is a limiting factor for investigating gravity wave dynamics as the spatial information is often missing, e.g., horizontal wavelength, propagation direction or intrinsic frequency. In this study, we present a new retrieval algorithm for multistatic meteor radar networks to obtain tomographic 3-D wind fields within a pre-defined domain area. The algorithm is part of the Agile Software for Gravity wAve Regional Dynamics (ASGARD) and called 3D-Var, and based on the optimal estimation technique and Bayesian statistics. The performance of the 3D-Var retrieval is demonstrated using two meteor radar networks: the Nordic Meteor Radar Cluster and the Chilean Observation Network De Meteor Radars (CONDOR). The optimal estimation implementation provide statistically sound solutions and diagnostics from the averaging kernels and measurement response. We present initial scientific results such as body forces of breaking gravity waves leading to two counter-rotating vortices and horizontal wavelength spectra indicating a transition between the rotational k−3 and divergent k-5/3 mode at scales of 80–120 km. In addition, we performed a keogram analysis over extended periods to reflect the latitudinal and temporal impact of a minor sudden stratospheric warming in December 2019. Finally, we demonstrate the applicability of the 3D-Var algorithm to perform large-scale retrievals to derive meteorological wind maps covering a latitude region from Svalbard, north of the European Arctic mainland, to central Norway. 

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3. Target Localization using Bistatic and Multistatic Radar with5GNR Waveform

   Ojas Kanhere, Sanjay Goyal (April 2021, IEEE Vehicular Technology Conference)

   Abstract—Joint communication an dsensing allows the utiliza- tion of common spectral resources for communication and local- ization, reducing the cost of deployment. By using fifth generation (5G) New Radio (NR)(i.e.,the 3rd Generation Partnership Project Radio Access Network for 5G) reference signals,conventionally used for communication,this paper shows sub-meter precision localization is possible at millimeter wave frequencies.We derive the geometric dilution of precision of a bistatic radar configura- tion, a theoretical metric that characterizes how the target location estimation error varies as a function of the bistatic geometry and measurement errors.We develop a 5GNR compliant software test bench to characterize the measurement errors when estimating the time difference of arrival and angle of arrival with5GNR waveforms.The test bench is further utilized to demonstrate the accuracy of target localization and velocity estimation in several indoor and outdoor bistatic and multistatic configurations and to show that on average,the bistatic cconfiguration can achieve a location accuracy of 10.0 cm over a bistatic range of 25m, which can be further improved by deploying a multistaticradar configuration. Index Terms—5G NR;Bistatic Radar;Multistatic Radar;ge- ometric dilution of precision (GDOP);3GPP;localization;posi- tioning; position location 

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4. A Machine Learning Algorithm to Detect and Analyze Meteor Echoes Observed by the Jicamarca Radar

   https://doi.org/10.3390/rs15164051  

   Li, Yanlin; Galindo, Freddy; Urbina, Julio; Zhou, Qihou; Huang, Tai-Yin (August 2023, Remote Sensing)

   We present a machine-learning approach to detect and analyze meteor echoes (MADAME), which is a radar data processing workflow featuring advanced machine-learning techniques using both supervised and unsupervised learning. Our results demonstrate that YOLOv4, a convolutional neural network (CNN)-based one-stage object detection model, performs remarkably well in detecting and identifying meteor head and trail echoes within processed radar signals. The detector can identify more than 80 echoes per minute in the testing data obtained from the Jicamarca high power large aperture (HPLA) radar. MADAME is also capable of autonomously processing data in an interferometer mode, as well as determining the target’s radiant source and vector velocity. In the testing data, the Eta Aquarids meteor shower could be clearly identified from the meteor radiant source distribution analyzed automatically by MADAME, thereby demonstrating the proposed algorithm’s functionality. In addition, MADAME found that about 50 percent of the meteors were traveling in inclined and near-inclined circular orbits. Furthermore, meteor head echoes with a trail are more likely to originate from shower meteor sources. Our results highlight the capability of advanced machine-learning techniques in radar signal processing, providing an efficient and powerful tool to facilitate future and new meteor research. 

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5. A Deep Reinforcement Learning Approach for Integrated Automotive Radar Sensing and Communication

   https://doi.org/10.1109/SAM53842.2022.9827815  

   Xu, Lifan; Zheng, Ruxin; Sun, Shunqiao (June 2022, IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM))

   We present a deep reinforcement learning approach to design an automotive radar system with integrated sensing and communication. In the proposed system, sparse transmit arrays with quantized phase shifter are used to carry out transmit beamforming to enhance the performance of both radar sensing and communication. Through interaction with environment, the automotive radar learns a reward that reflects the difference between mainlobe peak and the peak sidelobe level in radar sensing mode or communication user feedback in communication mode, and intelligently adjust its beamforming vector. The Wolpertinger policy based action-critic network is introduced for beamforming vector learning, which solves the dimension curse due to huge beamforming action space. 

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