中文摘要：

目的尽管传统的联合信源信道编码方案可以获得高效的压缩性能,但当信道恶化超过信道编码的纠错能力时会导致解码端重构性能的急剧下降;为此利用压缩感知的民主性提出一种鲁棒的SAR图像编码传输方案,且采用了一系列方法提高该方案的率失真性能。方法考虑到SAR图像丰富的边缘信息,采用具有更强方向表示能力的方向提升小波变换（DLWT）对SAR图像进行稀疏表示,且为消除压缩感知中恢复非稀疏信号时存在的混叠效应,采用了稀疏滤波方法保证大系数的精确恢复,在解码端采用了高效的Bayesian重建算法获得图像的高性能重建。结果在同等码率下,与传统的联合信源信道编码方案CCSDS-RS相比,本文方案可以实现更加鲁棒的编码传输,当丢包率达到0.05时,本文方案DSFB-CS获得的重建性能明显要高于CCSDS-RS;与基于Bayesian重建算法TSW-CS的传统方案相比,本文方案可提高峰值信噪比（PSNR）3.9 d B。结论本文方案DSFB-CS实现了SAR图像的鲁棒传输,随着丢包率的上升,DSFB-CS获得的重建性能缓慢下降,保证了面对不稳定信道时,解码端可以获得相对稳定的重构图像。

英文摘要：

Objective Consider a wireless communication system with a radio station in an airborne synthetic aperture radar （SAR） system operating in a time-varying channel. Unpredictable packet loss occurs during transmission. Therefore, building a robust and efficient SAR image coding transmission scheme is necessary. Although traditional joint source-chan- nel coding （JSCC） can achieve excellent and efficient transmission performance under fixed channel conditions, the prede- termined redundancy of channel coding was adopted to achieve robustness. However, when the deterioration of channel con- dition exceeds the correction capacity of the channel codec in a time-varying channel, reconstruction performance declines at the decoder. In this work, we propose a robust SAR image coding transmission scheme over a time-varying channel using the democracy of compressive sensing （CS） . A range of methods to improve the rate-distortion performance of the pro- posed scheme are also adopted. The reconstruction performance depends only on the number of measurements received and not on the actual measurements received; that is, every measurement is independent and nearly equal. Method Given the rich edge information of an SAR image, directional lifting wavelet transform （DLWT） is adopted as sparse representation to improve the representation of the edges of the SAR image. Although DLWT can attain good sparse representation for SAR images, this method cannot ensure strict sparse representation in CS; that is, representation coefficients still contain small coefficients that would interfere in the recovery of large coefficients. Thus, sparse filtering （ setting small coefficients to ze- ro） is also adopted in this study to eliminate the interference of small coefficients. Compared with the deterministic model- based CS reconstruction algorithms, the Bayesian model-based CS reconstruction algorithm is more reliable in a random sig- nal scenario. Thus, we adopt an efficient Bayesian reconstruction algorithm called tree