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自適應分割的雷達回波數(shù)據(jù)混合編碼算法

薛建儒,  鄭南寧,  權煒

薛建儒, 鄭南寧, 權煒. 自適應分割的雷達回波數(shù)據(jù)混合編碼算法[J]. 電子與信息學報, 2003, 25(8): 1059-1065.
引用本文: 薛建儒, 鄭南寧, 權煒. 自適應分割的雷達回波數(shù)據(jù)混合編碼算法[J]. 電子與信息學報, 2003, 25(8): 1059-1065.
Xue Jianru, Zheng Nanning, Quan Wei. Adaptive segmentation of radar return and hybrid coding scheme[J]. Journal of Electronics & Information Technology, 2003, 25(8): 1059-1065.
Citation: Xue Jianru, Zheng Nanning, Quan Wei. Adaptive segmentation of radar return and hybrid coding scheme[J]. Journal of Electronics & Information Technology, 2003, 25(8): 1059-1065.

自適應分割的雷達回波數(shù)據(jù)混合編碼算法

Adaptive segmentation of radar return and hybrid coding scheme

  • 摘要: 該文提出了一種基于自適應分割模型的雷達信號混合編碼算法。首先用自適應高斯混合體模型將雷達回波數(shù)據(jù)分為兩類;一類是由運動目標組成的前景圖;一類是視野中靜止(如礁石等)或具有重復性運動目標(如海浪等)的背景圖。然后對前景圖和背景圖分別用整數(shù)小波去相關,對變換后的前景圖和背景圖分別采用改進后的SPIHT無損壓縮和有損壓縮編碼。實驗表明,此編碼算法可有效降低數(shù)據(jù)傳輸率,在相同傳輸碼率下,相對于不分割直接壓縮算法EZW和SPIHT,重構信號的PSNR增加0.3-0.5dB,主觀質量也優(yōu)于同類算法。
    Abstract: In this paper, an adaptive segmentating, hybrid coding scheme of radar return is presented. Firstly, radar return is divided into two classes by adaptive Gaussian mixture model: foreground and background, the former is composed of moving targets, while the later is composed of still objects and repeat motion. Then Integer-to-integer wavelet transform is used for de-correlating the background and foreground, and an improved SPIHT codec is also used in coding wavelet coefficients of foreground in lossless way and wavelet coefficients of background in loss way. Experimental results show the scheme in this paper is better than codec based on SPIHT directly, and the PSNR of the reconstructed image is also increased about 0.3-0.5dB in average under the same bit rate compared with that of the latter one.
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  • J.L. Noga, Bayesian state-space modeling of Spatio-Temporal non-Gaussian radar return, [P. H.D. Dissertation], Signal Processing Lab, Department of Engineering, University of Cambridge,Dec. 1998.[2]C. Ridder.[J].et al., Adaptive background estimation and foreground detection using Kalmanfiltering, Proc. of International Conference on Recent Advance in Mechatronic, ICRAM95,Istanbul, Turkey.1995,:-[3]N. Friedman, S. Russell, Image segmentation in video sequences: A probabilistic approach, In Proc. of the Thirteen Conference on Uncertainty in Artificial Intelligence (UAI), Brown University, Providence, Rhode Island, USA. Aug. 1-3, 1997, 175-181.[4]J.M. Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE Trans. on Signal Processing, 1993, 41(12), 3445-3462.[5]A. Said, W. A. Pearman, A new fast and efficient image codec based on set partitioning in hierarchical tree, IEEE Trans. on Circuits and Syst. for Video Technol., 1996, 6(3), 243-250.[6]V.N. Ramaswamy, et al., Performance analysis of wavelets in embedded zerotree-based lossless image coding schemes, IEEE Trans. on Signal Processing, 1999, 47(3), 884-889.[7]Julien Reichel, Gloria Mengeaz, Marcus J. Nadenau, Integer wavelet transform for embedded lossy to lossless image compression, IEEE Trans. on Image Processing, 2001, 10(3), 383-392.[8]Xue Jianru.[J].Geng Xinli, Zheng Nanning, TWS tracking techniques based on adaptive Gaussian mixture model in phased array radar, Proc. of the 4th World Congress on Intelligent Control and Automation(WCICA02), Shanghai.2002,:-[9]G.K. Wallace, The JPEG Still Picture Compression Standard, Comm. ACM, 1991, 34(4), 30-44.[10]X. Wu, N. Memon, Context-based, adaptive, lossless image codec, IEEE Trans. on Comm., 1997,45(4), 437-444.
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出版歷程
  • 收稿日期:  2002-03-15
  • 修回日期:  2002-09-23
  • 刊出日期:  2003-08-19

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