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1. AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

   https://doi.org/10.3390/s19245479  

   Rahnemoonfar, Maryam ; Johnson, Jimmy ; Paden, John ( December 2019 , Sensors)

   Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Dopplermore »effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.« less
2. Comparisons between high-resolution profiles of squared refractive index gradient <i>M</i>² measured by the Middle and Upper Atmosphere Radar and unmanned aerial vehicles (UAVs) during the Shigaraki UAV-Radar Experiment 2015 campaign

   https://doi.org/10.5194/angeo-35-423-2017  

   Luce, Hubert ; Kantha, Lakshmi ; Hashiguchi, Hiroyuki ; Lawrence, Dale ; Yabuki, Masanori ; Tsuda, Toshitaka ; Mixa, Tyler ( January 2017 , Annales Geophysicae)

   Abstract. New comparisons between the square of the generalized potential refractive index gradient M2, estimated from the very high-frequency (VHF) Middle and Upper Atmosphere (MU) Radar, located at Shigaraki, Japan, and unmanned aerial vehicle (UAV) measurements are presented. These comparisons were performed at unprecedented temporal and range resolutions (1–4 min and  ∼  20 m, respectively) in the altitude range  ∼  1.27–4.5 km from simultaneous and nearly collocated measurements made during the ShUREX (Shigaraki UAV-Radar Experiment) 2015 campaign. Seven consecutive UAV flights made during daytime on 7 June 2015 were used for this purpose. The MU Radar was operated in range imaging mode for improving the range resolution at vertical incidence (typically a few tens of meters). The proportionality of the radar echo power to M2 is reported for the first time at such high time and range resolutions for stratified conditions for which Fresnel scatter or a reflection mechanism is expected. In more complex features obtained for a range of turbulent layers generated by shear instabilities or associated with convective cloud cells, M2 estimated from UAV data does not reproduce observed radar echo power profiles. Proposed interpretations of this discrepancy are presented.
3. A Novel Radar Imaging Method Based on Random Illuminations Using FMCW Radar

   https://doi.org/10.1109/WiSNeT46826.2020.9037583  

   Nallabolu, Prateek ; Li, Changzhi ( January 2020 , 2020 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT))
4. OFDM Pilot-Based Radar for Joint Vehicular Communication and Radar Systems

   https://doi.org/10.1109/VNC.2018.8628347  

   Ozkaptan, Ceyhun D. ; Ekici, Eylem ; Altintas, Onur ; Wang, Chang-Heng ( December 2018 , 2018 IEEE Vehicular Networking Conference (VNC))

   With the large-scale deployment of connected and autonomous vehicles, the demand on wireless communication spectrum increases rapidly in vehicular networks. Due to increased demand, the allocated spectrum at the 5.9 GHz band for vehicular communication cannot be used efficiently for larger payloads to improve cooperative sensing, safety, and mobility. To achieve higher data rates, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz band can be exploited for communication. However, instead of employing spectral isolation or interference mitigation schemes between communication and radar, we design a joint system for vehicles to perform both functions using the same waveform. In this paper, we propose radar processing methods that use pilots in the orthogonal frequency-division multiplexing (OFDM) waveform. While the radar receiver exploits pilots for sensing, the communication receiver can leverage pilots to estimate the time-varying channel. The simulation results show that proposed radar processing can be efficiently implemented and meet the automotive radar requirements. We also present joint system design problems to find optimal resource allocation between data and pilot subcarriers based on radar estimation accuracy and effective channel capacity.
5. Deep Radar Waveform Design for Efficient Automotive Radar Sensing

   https://doi.org/10.1109/SAM48682.2020.9104367  

   Khobahi, Shahin ; Bose, Arindam ; Soltanalian, Mojtaba ( June 2020 , 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM))

   2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)