Journal of Satellite, Information and Communications (한국위성정보통신학회논문지)

Korea Society of Satellite Technology (한국위성정보통신학회)
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Compressed Sensing Techniques for Video Transmission of Multi-Copter

멀티콥터 영상 전송을 위한 압축 센싱 기법

  • 정국현 (광운대학교 전파공학과, 유비쿼터스 통신 연구실) ;
  • 이선의 (광운대학교 전파공학과, 유비쿼터스 통신 연구실) ;
  • 이상화 (서울대학교 전자컴퓨터공학부) ;
  • 김진영 (광운대학교 전파공학과, 유비쿼터스 통신 연구실)
  • Received : 2014.05.10
  • Accepted : 2014.05.30
  • Published : 2014.06.30
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Abstract

This paper proposed a novel compressed sensing (CS) technique for an efficient video transmission of multi-copter. The proposed scheme is focused on reduction of the amount of data based on CS technology. First, we describe basic principle of Spectrum sensing. And then we compare AMP(Approximate Message Passing) with CoSaMP(Compressive Sampling Matched Pursuit) through mathematical analysis and simulation results. They are evaluated in terms of calculation time and complexity, then the promising algorithm is suggestd for multicopter operation. The result of experiment in this paper shows that AMP algorithm is more efficient than CoSaMP algorithm when it comes to calculation time and image error probability.

본 논문에서는 멀티콥터의 효율적 영상 전송을 위해 필요한 압축센싱 기법을 제안한다. 제안된 구조는 압축센싱에 기반한 데이터 용량을 줄이는 것에 중점을 둔다. 우선 Spectrum sensing의 기본원리를 설명하고 AMP(Approximate Message Passing)와 CoSaMP(Compressive Sampling Matched Pursuit)을 수학적 분석과 모의실험 결과를 통해서 비교한다. 또한 두 알고리즘을 계산시간과 복잡도 관점에서 평가하고 멀티콥터 동작에 적합한 알고리즘을 제안한다. 본 논문의 실험결과는 AMP 알고리즘이 CoSaMP 알고리즘보다 계산시간이 적고 이미지 에러 확률도 낮다는 것을 보여준다.

Keywords

References

  1. J. Y. Kim, Cognitive Radio Communication, Inter Vision, 2008.
  2. D. L. Donoho, "Compressed sensing,"IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006. https://doi.org/10.1109/TIT.2006.871582
  3. D. L. Donoho and J. Tanner, "Precise undersampling theorems,"IEEE Signal Process. Mag., vol. 98, no. 6, pp. 913-924, June. 2010.
  4. I. F. Akyildiz, W. Y. Lee, M. C. Vuran and S. Mohanty, "NeXt generation/dynamic spectrum access/cognitive radio wireless networks," a survey, Computer Networks, vol. 50, no. 13, pp. 2127-2159, 2006. https://doi.org/10.1016/j.comnet.2006.05.001
  5. D. Cabric, S. Mishra, R. Brodersen, "Implementation issues in spectrum sensing for cognitive radios," in: Proc. of Asilomar Conf. on Signals, Systems and Computers, vol. 1, pp. 772-776, 2004.
  6. Zhu Han, Husheng Li and Wotao Yin, Compressive sensing for wireless networks, Cambridge, 2013.
  7. Y.C. Pati, R. Rezaifar, and P.Krishnaprasad, "Orthogonal matching pursuit : Recursive function approximation with applications to wavelet decomposition," in Asilomar Conf. Signals, Systems and Computation, vol. 1, (Pacific Grove, CA), pp. 40-44, Nov. 1993.
  8. D. Needell and J. A. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples,"Appl. Comput. Harmon. Anal., vol. 26, no. 3, pp. 301-321, 2009. https://doi.org/10.1016/j.acha.2008.07.002