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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-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-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。
Traditional signal detection algorithms are based on the Nyquist sampling theorem, which is hard to implement in hardware due to the high sampling rate required for very wide bandwidth ultra-wideband (UWB) signals. Therefore, this paper investigates the signal detection problem of impulse radio UWB (IR-UWB) based on compressive sensing (CS). Based on the sparse characteristics of IR-UWB signal in the time domain, Compressed sensing IR-UWB signal detection framework, based on which an adaptive weighted orthogonal matching pursuit detection algorithm is proposed. The simulation results show that the new algorithm not only can detect IR-UWB signals by far less than the sampling rate required by the Nyquist theorem, but also compared with the compression-aware detection algorithm based on matching pursuit. The case of IR-UWB signal detection better.