차세대 지능형 레이다 발전 방안

  • Published : 2019.03.31

Abstract

Keywords

References

  1. T. John, X. Ge, H.-C. Wu, R. Irmer, H. Jiang, G. Fettweis, and S. Alamouti, "5G wireless communication systems: Prospects and challenges", IEEE Communications Magazine, vol. 52, no. 2, pp. 62-64, 2014. https://doi.org/10.1109/MCOM.2014.6736744
  2. T. Magedanz, "5G technologies and smart cities", http://www.opengroup.org, Jun. 2017.
  3. Fraunhofer FOKUS, "The 5G playground: Be part of the 5G evolution", 2015.
  4. "5G Mobile Communications for 2020 and Beyond-Vision and Key Enabling Technologies", EUCNC, Jun. 2014.
  5. R. J. Carlos, J. Ribeiro, J. Rodriguez, R. Dionisio, H. Esteves, P. Duarte, and P. Marques, "Testbed for combination of local sensing with geolocation database in real environments", IEEE Wireless Communications, vol. 19, no. 4, pp. 59-66, 2014. https://doi.org/10.1109/MWC.2012.6272424
  6. D. Pastina, F. Colone, T. Martelli, and P. Falcone, "Parasitic exploitation of Wi-Fi signals for indoor radar surveillance", IEEE Trans. Vehicu. Techn., vol. 64, no. 4, pp. 1401-1415, Apr. 2015. https://doi.org/10.1109/TVT.2015.2392936
  7. F. Guidi, A. Guerra, and D. Dardari, "Personal mobile radars with millimeter-wave massive arrays for indoor mapping", IEEE Trans. Mobile Comput., vol. 15, no. 6, pp. 1471-1484, Jun. 2016. https://doi.org/10.1109/TMC.2015.2467373
  8. F. Adib, H. Mao, Z. Kabelac, D. Katabi, R. C. Miller, "Smart homes that monitor breathing and heart rate", CHI Conference, pp. 837-846, Apr. 2015.
  9. I. V. Mikhelson, S. Bakhtiari, T. W. Elmer, II, and A. V. Sahakian, "Remote sensing of heart rate and patterns of respiration on a stationary subject using 94-GHz millimeterwave interferometry", IEEE Trans. Biomed. Eng., vol. 58, no. 6, pp. 1671-1677, Jun. 2011. https://doi.org/10.1109/TBME.2011.2111371
  10. E. Schires, P. Georgiou, and T. S. Lande, "Vital sign monitoring through the back using an UWB impulse radar with body coupled antennas", IEEE Trans. Biomed. Circuits Syst., vol. 12, no. 2, pp. 292-302, Apr. 2018. https://doi.org/10.1109/TBCAS.2018.2799322
  11. T.-H. Liu, M.-L. Hsu, and Z.-M. Tsai, "High ranging accuracy and wide detection range interferometry based on frequency-sweeping technique with vital sign sensing function", IEEE Microw. Theory. Techn., vol. 66, no. 9, pp. 4242-4251, Sep. 2018. https://doi.org/10.1109/TMTT.2018.2854177
  12. C. Gu, Z. Peng, and C. Li, "High-precision motion detection using low complexity Doppler radar with digital post-distortion technique", IEEE Trans. Microw. Theory Techn., vol. 64, no. 3, pp. 961-971, Mar. 2016. https://doi.org/10.1109/TMTT.2016.2519881
  13. P. Molchanov, K. Egiazarian, J. Astola, R. I. A. Harmanny, and J. J. M. de Wit, "Classification of small UAVs and birds by micro-Doppler signatures", 2013 European Radar Conference, Nuremberg, pp. 172-175, 2013.
  14. P. Zhang, L. Yang, G. Chen, and G. Li, "Classification of drones based on micro-Doppler signatures with dualband radar sensors", 2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL), Singapore, pp. 638-643, 2017.
  15. W. Zhang, G. Li, "Detection of multiple micro-drones via cadence velocity diagram analysis", in Electronics Letters, vol. 54, no. 7, pp. 441-443, Apr.-May 2018. https://doi.org/10.1049/el.2017.4317
  16. S. Bjorklund, "Target detection and classification of small drones by boosting on radar micro-Doppler", 2018 15th European Radar Conference (EuRAD), Madrid, pp. 182-185, 2018.
  17. B. Oh, X. Guo, F. Wan, K. Toh, and Z. Lin, "Micro-Doppler mini-UAV classification using empirical-mode decomposition features", in IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 227-231, Feb. 2018. https://doi.org/10.1109/LGRS.2017.2781711
  18. B. K. Kim, H. Kang, and S. Park, "Experimental analysis of small drone polarimetry based on micro-Doppler signature", in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 10, pp. 1670-1674, Oct. 2017. https://doi.org/10.1109/LGRS.2017.2727824
  19. E. M. Staderini, "UWB radars in medicine", IEEE Aerosp. Electron. Syst. Mag., vol. 17, no. 1, pp. 13-18, Jan. 2002. https://doi.org/10.1109/62.978359
  20. A. B. Blyakhman, I. A. Runova, "Bistatic radar cross section and the detection of objects from their forward scatter", Journal of Communications Technology and Electronics, vol. 46, no. 4, pp. 393-401, Apr. 2001.
  21. E. E. Laubie, B. D. Rigling, and R. P. Penno, "Bistatic aspect diversity for improved SAR target recognition", 2015 IEEE Radar Conference (RadarCon). 2015.
  22. G. Krieger, A. Moreira, "Spaceborne bi-and multistatic SAR:Potential and challenges", IEEE Proceedings of Radar, Sonar and Navigation, vol. 153, no. 3, pp. 184-198, Jun. 2006. https://doi.org/10.1049/ip-rsn:20045111
  23. S. Haykin, Adaptive Radar Signal Processing, Wiley, 2007.
  24. S. Haykin, Neural Networks and Learning Machines, Upper Saddle River, NJ: Prentice-Hall, 2009.
  25. S. Haykin, D. J. Thomson, and J. H. Reed, "Spectrum sensing for cognitive radio", Proceedings of the IEEE, vol. 97, pp. 849-877, 2009. https://doi.org/10.1109/JPROC.2009.2015711
  26. S. Haykin, A. Zia, Y. Xue, and I. Arasaratnam, "Controltheoretic approach to tracking radar: first step towards cognition", Digital Signal Processing, vol. 21, pp. 576-585, 2011. https://doi.org/10.1016/j.dsp.2011.01.004
  27. S. Haykin, "Cognitive radar", IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30-40, Jan. 2006. https://doi.org/10.1109/MSP.2006.1593335