基于先验信息压缩感知SAR成像的信息理论限及实用算法研究

项目名称： 基于先验信息压缩感知SAR成像的信息理论限及实用算法研究

项目编号： No.61501485

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 周汉飞

作者单位： 深圳大学

项目金额： 18万元

中文摘要： 将目标信号先验概率及结构性先验信息嵌入压缩感知SAR成像系统，对改善成像性能和提升算法鲁棒性具有重要意义。本项目以压缩感知SAR成像为背景，通过建立雷达目标先验概率和结构相关信息模型，推导先验信息稀疏恢复的信息理论限，并设计嵌入先验信息的实用稀疏恢复算法。研究围绕基于先验信息稀疏恢复的信息理论限展开，包含相互联系的三个环节。其一，先验信息模型构建是前提。通过SAR目标特性分析和自适应学习方法确定目标先验概率，用树稀疏模型描述目标结构相关信息。其二，信息理论限推导是重点。基于先验信息模型，利用典型集理论和信道编码定理，推导基于先验信息稀疏恢复的信息理论限。其三，实用算法设计是关键。以信息理论限为指导，借助稀疏恢复中的权重系数，设计将先验信息嵌入算法流程的实用稀疏恢复算法。本项目研究不仅支撑压缩SAR成像，对指导基于压缩感知的雷达系统设计也具备重要指导价值。

中文关键词： SAR成像；压缩感知；稀疏恢复；先验信息；信息理论限

英文摘要： Embedding the target signal and structural prior information into CS-based SAR system is valuable to improve performance and robustness of SAR imaging. For the CS-based SAR imaging, the prior probability and structural information model of SAR object are established and the information-theoretic limits with prior information is deduced, then practical algorithm is designed to embed prior information. Rounding the information-theoretic limits of sparsity recovery with prior information, this research includes three associated parts. Firstly, the establishing of prior information model is groundwork. The prior probability is established by analyzing and adaptive study of object feature. The structural information is represented by tree-sparse model. Secondly, the deducing of information-theoretic limits is important. Based on prior information model, the information-theoretic limits is deduced by typical set theory and channel coding theorem. Lastly, the designing of practical algorithm is crucial. Guiding by information-theoretic limits, the practical sparsity recovery algorithm which embeds the prior information is designed by setting the weights. The research not only is very important for CS-based SAR imaging, but also is very valuable for CS-based radar system designing.

英文关键词： SAR imaging;compressive sensing;sparse recovery;prior information;information-theoretic limits