自适应分割的雷达回波数据混合编码算法

薛建儒; 郑南宁; 权炜

自适应分割的雷达回波数据混合编码算法

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出版历程
- 收稿日期: 2002-03-15
- 修回日期: 2002-09-23
- 刊出日期: 2003-08-19

Adaptive segmentation of radar return and hybrid coding scheme

摘要

摘要: 该文提出了一种基于自适应分割模型的雷达信号混合编码算法。首先用自适应高斯混合体模型将雷达回波数据分为两类;一类是由运动目标组成的前景图;一类是视野中静止(如礁石等)或具有重复性运动目标(如海浪等)的背景图。然后对前景图和背景图分别用整数小波去相关,对变换后的前景图和背景图分别采用改进后的SPIHT无损压缩和有损压缩编码。实验表明,此编码算法可有效降低数据传输率,在相同传输码率下,相对于不分割直接压缩算法EZW和SPIHT,重构信号的PSNR增加0.3-0.5dB,主观质量也优于同类算法。
- 自适应高斯混合体; 信号压缩; 数据分割; 整数小波变换
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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参考文献(1)

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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