中文摘要：

压缩感知理论为IR-UWB信号的低速采样接收提供了新的思路，但现有的低速率压缩采样架构大都理想化了量化过程。该文充分考虑量化噪声的实际影响，拟设计出抗噪性强的IR-UWB接收信号重构方法。基于对压缩采样值中噪声分布特性的分析，修正了信号重构模型，并通过仿真对比了DS（Dantzig—Selector）法求解和传统重构算法求解的性能差异。在此基础上，提出了一种在DS和SP（Subspace Pursuit）算法中自适应选择的信号重构方法（联合DS—SP）。仿真结果表明，联合DS—SP以折中于DS和SP之间的复杂度在不同噪声情形下获得了最优的重构性能，且相对经典重构算法有较大的性能提升，为压缩感知框架下的IR-UWB接收机数字后端提供了一种新的信号重构策略。

英文摘要：

Compressed Sensing （CS） theory provides a new solution for low-rate sampling design of Impulse Radio Ultra-WideBand （IR-UWB） receiver, but the quantization process is usually idealized in existent CS based sampling architectures. In this paper, the influence of quantization noise is fully considered, and an IR-UWB signal reconstruction method with high anti-noise performance is proposed. Based on the analysis of the receiver noise distribution characteristics, the signal reconstruction optimization model is revised, and then the performance of Dantzig-Selector （DS） method is compared with the traditional signal reconstruction algorithms. Further, a joint DS-SP method which can self-adaptively select the reconstruction algorithms between DS and SP （Subspace Pursuit） is proposed. Simulation results show that the joint DS-SP method which has computational complexity trade-off between DS and SP can get the best performance under different noise regions and quantization precisions What＇s more, joint DS-SP has large performance improvement compared to the traditional reconstruction algorithms, thus provides a new strategy of CS signal reconstruction for the design of IR-UWB receiver＇s digital back-end.