基于压缩感知联合稀疏重构的宽带阵列信号处理技术

项目名称： 基于压缩感知联合稀疏重构的宽带阵列信号处理技术

项目编号： No.61201274

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 段惠萍

作者单位： 电子科技大学

项目金额： 27万元

中文摘要： 基于压缩感知稀疏重构框架的宽带阵列信号处理技术可以避开子空间方法的主要缺陷，但是现有技术仍存在稀疏表达模型不完善、低信噪比环境下重构性能不理想、对阵列及建模误差敏感等问题,本项目为宽带阵列信号建立恰当的联合稀疏表达模型，突破所有窄带分量共享相同稀疏结构的假设从而改善已有算法的重构性能，采用贪婪迭代、非凸优化和贝叶斯的联合稀疏重构方法从不同角度满足阵列处理对复杂度、重构效率、收敛性能等方面的要求；建立确定性和随机性的基不匹配模型，通过误差校准和稀疏信号联合交替更新来改善稳健性。主要内容有：宽带阵列联合稀疏表达模型和联合稀疏重构算法的研究，基不匹配模型和稳健的联合稀疏重构算法的研究，对方向可辨识条件和波形可恢复条件的理论研究以及对算法性能的理论和仿真分析验证。本项目的研究将从建模、算法和理论三个方面推动和完善基于压缩感知联合稀疏重构的宽带阵列信号处理技术的研究，为该领域提供具有原创性的成果。

中文关键词： 稀疏重构；宽带阵列信号处理；压缩感知；优化；稳健性

英文摘要： CS(Compressive-Sensing)-based wideband array signal processing techniques using the framework of sparse reconstruction can avoid the main drawbacks of subspace-based methods. However, current CS-based approaches still suffer from a few limitations, such as lacking of suitable sparse representation models, undesirable reconstruction performance in enviroments with low signal-to- noise ratio, sensitivity to the array and model errors and so on. In this project, suitable joint sparse representation models will be built for wideband array signals. The assumption about same sparsity structure shared by all narrowband components in existing algorithms will be broken to improve the reconstruction performance. Some novel and effective joint sparse reconstruction algorithms belonging to the greedy iteration, non-convex optimization and Bayesian methods will be developed to meet the requirments of array processing on computational complexity, reconstruction efficiency and convergence tendency respectively. Besides, the deterministic and stochastic models will be built for basis mismatch caused by practical errors. The joint sparse reconstruction methods performing error calibration and sparse signal reconstruction alternatively will be investigated to improve the robustness. The main contents are as follows: the investiga

英文关键词： sparse recovery；wideband array signal processing；compressive sensing；optimization；robustness