首页 | 本学科首页   官方微博 | 高级检索  
     

星载SAR原始数据普适性分块自适应矢量压缩算法
引用本文:祁海明,华斌,李信,禹卫东,洪文. 星载SAR原始数据普适性分块自适应矢量压缩算法[J]. 中国科学:信息科学, 2012, 0(2): 206-217
作者姓名:祁海明  华斌  李信  禹卫东  洪文
作者单位:中国科学院电子学研究所;微波成像技术国家重点实验室
基金项目:中国科学院优秀博士论文院长奖获得者专项基金(批准号:0813260042);微波成像技术国家重点实验室基金(批准号:9140C1903041003)资助项目
摘    要:
传统矢量量化器的码本普适性差,需要在线更新,难以在星载SAR系统中实现.文中针对星载SAR原始数据的统计特性,以多维空间上的失真函数为代价函数,根据输入数据的联合概率密度函数设计得到了具有普适性的矢量量化码本,分析了原始数据矢量量化编码以及解码方案.在此基础上,深入研究了矢量量化级联熵编码方案的可行性以及码字索引在信道传输发生误码时算法的稳健性.实际数据处理结果表明,文中算法具有普适性,矢量量化码本的普适性使得码本可以进行离线设计,为矢量量化的星载实用化提供了理论指导.

关 键 词:合成孔径雷达  压缩  原始数据  分块自适应量化  矢量量化

A universal adaptive vector quantization algorithm for space-borne SAR raw data
QI HaiMing,HUA Bin,LI Xin,YU WeiDong,, HONG Wen. A universal adaptive vector quantization algorithm for space-borne SAR raw data[J]. Scientia Sinica Informationis, 2012, 0(2): 206-217
Authors:QI HaiMing  HUA Bin  LI Xin  YU WeiDong  & HONG Wen
Affiliation:1,2 1 Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China;2 The National Key Laboratory of Microwave Imaging Technology,Beijing 100190,China
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.
Keywords:synthetic aperture radar(SAR)  compression  raw data  block adaptive quantization(BAQ)  vector quantization(VQ)
本文献已被 CNKI 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号