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http://dx.doi.org/10.5139/JKSAS.2021.49.11.933

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)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.49, no.11, 2021 , pp. 933-940 More about this Journal
Abstract
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.
Keywords
Block Adaptive Quantization; Quantization Error; Synthetic Aperture Radar; Digital Receiver; SNR Degradation;
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1 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.
2 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.
3 Lancashire, D. C., Barnes, B. A. F. and Udall, S. J., "Block Adaptive Quantization," U.S. Patent 6 255 987, July 3, 2001.
4 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.   DOI
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.   DOI
7 Lloyd, S. P., "Least Squares Quantization in PCM," IEEE Transactions on Information Theory, Vol IT-28, No. 2, 1982, pp. 129~137.   DOI
8 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.   DOI
9 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.   DOI