한국항공우주학회지 (Journal of the Korean Society for Aeronautical & Space Sciences)

  • 제49권11호
  • /
  • Pages.933-940
  • /
  • 2021
  • /
  • 1225-1348(pISSN)
  • /
  • 2287-6871(eISSN)
한국항공우주학회 (The Korean Society for Aeronautical and Space Sciences)
권호 표지
DOI QR코드

DOI QR Code

위성탑재 영상레이다 디지털 수신기에서의 양자화 영향성 분석

Digitization Impact on the Spaceborne Synthetic Aperture Radar Digital Receiver Analysis

  • Lim, Sungjae (The Defense Space Technology Center, Agency for Defense Development) ;
  • Lee, Hyonik (The Defense Space Technology Center, Agency for Defense Development) ;
  • Sung, Jinbong (The Defense Space Technology Center, Agency for Defense Development) ;
  • Kim, Seyoung (The Defense Space Technology Center, Agency for Defense Development)
  • 투고 : 2021.08.24
  • 심사 : 2021.10.18
  • 발행 : 2021.11.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

위성탑재 영상레이다 시스템은 마이크로파를 방사하여 지상에서 되튕겨온 신호를 수신한다. 수신된 신호는 영상레이다 수신경로의 마지막에 위치한 디지털 수신기에서 디지털 신호로 변환된다. 변환된 디지털 신호는 필터링, 압축 및 포맷팅 과정을 거친다. 디지털 수신기의 신호처리 과정은 두 차례의 양자화로 수행된다. 첫 번째는 아날로그 신호를 디지털 신호로 변환하는 과정이고, 다른 하나는 BAQ를 이용한 압축과정이다. 양자화는 높은 비트에서 낮은 비트로 변환하는 과정으로 양자화 오차가 발생한다. 본 논문에서는 SNR 저하의 관점에서 디지털 수신기에서 수행되는 양자화의 영향성을 분석하였다.

The space-borne SAR(Synthetic Aperture Radar) system radiates the microwave signal and receives the backscattered signal. The received signal is converted to digital at the Digital Receiver, which is implemented at the end of the SAR sensor receiving chain. The converted signal is formated after signal processing such as filtering and data compression. Two quantization are conducted in the Digital Receiver. One quantization is an analog to digital conversion at ADC(Analog-Digital Converter). Another quantization is the BAQ(Block Adaptive Quantization) for data compression. The quantization process is a conversion from a continuous or higher bit precision to a discrete or lower bit precision. As a result, a quantization noise is inevitably occurred. In this paper, the impact of two quantization processes are analyzed in a view of SNR degradation.

키워드

참고문헌

  1. Lee, B. H. and Jeong, H., "The Analysis of Statistical Properties of Quantization of ERS-1 SAR(Synthetic Aperture Radar) Raw Data," IEIE Annual conference Proceedings, Vol. 20, No. 2, 1997, pp. 1225~1228.
  2. Lancashire, D. C., Barnes, B. A. F. and Udall, S. J., "Block Adaptive Quantization," U.S. Patent 6 255 987, July 3, 2001.
  3. Kwok, R. and Johnson, W. T. K., "Block Adaptive Quantization of Magellan SAR Data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 27, No. 4, 1989, pp. 375~383. https://doi.org/10.1109/36.29557
  4. Lim, S. J., Lee, H. I., Kim, S. Y. and Nam, C. H., "An Optimization Method for BAQ(Block Adaptive Quantization) Threshold Table Using Real SAR Raw Data," Journal of the Korea Institute of Military Science and Technology, Vol. 20, No. 2, 2017, pp. 187~196. https://doi.org/10.9766/KIMST.2017.20.2.187
  5. Younis, M., Boer, J., Ortega, C., Schulze, D., Huber, S. and Mittermayer, J., "Determining the Optimum Compromise between SAR Data Compression and Radiometric Performance-An Approch Based on the Analysis of TerraSAR-X Data," IGARSS 2008, IEEE International Geoscience Remote Sensing Symposium, 2008, pp. III-107~III-110.
  6. Martone, M., Brautigam, B. and Krieger, G., "Quantization Effects in TanDEM-X Data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 2, 2015, pp. 583~597. https://doi.org/10.1109/tgrs.2014.2325976
  7. Zan, F. D. and Guarnieri, A. M., "TOPSAR: Terrain Observation by Progressive Scans," IEEE Transactions on Geoscience and Remote Sensing, Vol 44, No. 9, 2006, pp. 2352~2360. https://doi.org/10.1109/TGRS.2006.873853
  8. Hasso, A., Jacksi, K. and Smith, K., "Effect of Quantization Error and SQNR on the ADC Using Truncating Method to the Nearest Integer Bit," ICOASE 2019, International Conference on Advanced Science and Engineering, 2019, pp. 112~117.
  9. Lloyd, S. P., "Least Squares Quantization in PCM," IEEE Transactions on Information Theory, Vol IT-28, No. 2, 1982, pp. 129~137. https://doi.org/10.1109/TIT.1982.1056489