Abstract
Codebook of conventional VQ cannot be generally used and needs real time onboard updating, which is hard to implement in spaceborne SAR system. In order to solve this problem, this paper analyses the characteristic of space-borne SAR raw data firstly, and then utilizes the distortion function of multidimensional space as criterion, and finally the adaptive code book design algorithm is proposed according to the joint probability density function of the input data. Besides, the feasibility of the new algorithm in cascade with entropy coding and the robustness of the algorithm when error occurs during transmission are analysed based on the encoding and decoding scheme. Experimental results of real data show that codebook deriving from the new algorithm can be generally used and designed off-line, which makes VQ a practical algorithm for space-borne SAR raw data compression.
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References
Kwok R, Johnson W T K. Block adaptive quantization of Magellan SAR data. IEEE Trans Geosci Remote, 1989, 27: 375–383
Boustani A E, Branham K, Kinsner W. A review of current raw SAR data compression techniques. In: Canadian Conference on Electrical and Computer Engineering 2001. Toronto: IEEE, 2001. 925–930
Benz U, Strodl K, Moreria A. A comparison of several algorithms for SAR raw data compression. IEEE Trans Geosci Remote, 1995, 33: 1266–1276
Snoeij P, Attema E, Guarnieri A M, et al. GMES Sentinel-1 FDBAQ performance analysis. In: 2009 IEEE Radar Conference. California: IEEE, 2009. 1–6
Snoeij P, Attema E, Guarnieri A M, et al. FDBAQ a novel encoding scheme for Sentinel-1. In: Proceedings of IGARSS 2009. Cape Town, 2009. I-44-I-47
Agrawal N, Venugopalan K. Amplitude phase algorithm for SAR signal processing. In: Proceedings of the 2009 1st International Conference on Computational Intelligence, Communication Systems and Networks. Washington DC: IEEE, 2009. 351–356
Linde Y, Buzo A, Gray R M. An algorithm for vector quantizer design. IEEE Trans Commun, 1980, 28: 84–95
Lebedeff D, Mathieu P, Barlaud E, et al. Adaptive vector quantization for raw SAR data. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. Detroit: IEEE, 1995. 2511–2514
Zhao D, Samuelsson J, Nilsson M. On entropy-constrained vector quantization using Gaussian mixture models. IEEE Trans Commun, 2008, 56: 2094–2104
Fischer J, Benz U, Moreira A. Efficient SAR raw data compression in frequency domain. In: Proceedings of IGARSS99. Hamburg: IEEE 1999. 2261–2263
Bhattacharya S, Blumensath T, Mulgrew B, et al. Fast encoding of synthetic aperture radar raw data using compressed sensing. In: IEEE Workshop on Statistical Signal. Madison: IEEE, 2007. 448–452
Herman M A, Strohmer T. High-resolution radar via compressed sensing. IEEE Trans Signal Proces, 2009, 57: 2275–2284
Qi H M, Yu W D. Study of effect of raw data compression on space-borne InSAR interferometry based on real data (in Chinese). J Electron Inform Technol, 2008, 30: 2693–2697
Qi H M, Yu W D, Chen X. Piecewise linear mapping algorithm for SAR raw data compression. Sci China Ser F-Inf Sci, 2008, 51: 2126–2134
Qi H M, Yu W D. Anti-saturation block adaptive quantization algorithm for SAR raw data compression over the whole set of saturation degrees. Prog Nat Sci, 2009, 19: 1003–1009
Chen D T S. On two or more dimensional optimum quantizers. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. Hartford: IEEE, 1977. 640–643
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Qi, H., Hua, B., Li, X. et al. A universal adaptive vector quantization algorithm for space-borne SAR raw data. Sci. China Inf. Sci. 55, 1280–1289 (2012). https://doi.org/10.1007/s11432-011-4316-6
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DOI: https://doi.org/10.1007/s11432-011-4316-6