基于压缩感知的脉冲超宽带信号检测

传统的信号检测算法基于奈奎斯特采样定理来实现,这对于带宽极宽的超宽带(ultra-wideband,UWB)信号而言由于要求采样速率过高而很难用硬件去实现。为此,本文研究了基于压缩感知(compressive sensing,CS)的脉冲超宽带(impulse radio UWB, IR-UWB)信号检测问题,利用IR鄄UWB 信号在时域上的稀疏特性,设计了一种基于压缩感知的IR鄄UWB 信号检测框架,在此基础上提出了一种自适应加权正交匹配追踪检测算法。仿真结果表明,新算法不仅能够通过远少于奈奎斯特定理所要求的采样速率检测出IR-UWB 信号,而且与基于匹配追踪的压缩感知检测算法相比,新算法在低信噪比的情况下对IR-UWB 信号的检测效果更佳。

Impulse Radio Ultra-Wideband Signal Detection Based on Compressive Sensing

It is well known that the traditional signal detection methods are based on the Nyquist theorem. However, it will lead to an extremely high sampling rate for ultra-wideband signal detection, making it difficult to implement in practical hardwares. In order to solve this problem, impulse radio UWB (IR-UWB) signal detection problem based on compressive sensing (CS) is studied in this paper. By exploiting the sparsity of the IR-UWB signal in the time domain, we design a CS-based IR-UWB signal detection system and propose an adaptive weighted orthogonal matching pursuit (AWOMP) algorithm.Simulation results show that the proposed algorithm can detect the IR-UWB signal with a sampling rate much lower than the Nyquist rate. Moreover, it can maintain higher performance gains than the existing matching pursuit (MP) based detection algorithm, especially at low signal-to-noise ratio (SNR) egimes.