摘要
针对低信噪比条件下现有压缩感知系统重构性能严重恶化的问题,提出了一种基于选择性测量的自适应压缩感知结构.首先推导并分析了经过压缩测量的噪声的统计特性及其对重构性能的影响;然后基于输出能量最小化准则,设计了一种压缩域投影滤波联合噪声检测的自适应感知器,感知获得噪声子空间的位置信息;进一步利用该信息构造选择性压缩测量矩阵,智能选择测量信号,同时'屏蔽'噪声分量,极大提高了压缩测量值的信噪比.仿真结果表明,相对于现有压缩感知结构,选择性测量的压缩感知结构明显改善了含噪稀疏信号的重构性能,可更好地应用于吸波材料的前端特性分析、认知无线电的频谱感知等领域.
An adaptive compressed sensing architecture based on selective measure is proposed in this paper, in order to reduce the effects of non-sparse noise component on the performance of existing compressed sensing reconstruction algorithm. Firstly, in this paper we analyze and deduces the statistics characteristic of the measured noise and its influence on the reconstruction performance; then we propose a compressive-domain projection filter combined with iterative noise detector method to obtain the location information of noise subspace based on minimal output energy criteria; thirdly, we measure matrix adaptively with the location information, and focus on the signal subspace directly without sensing the noise component in analog part. Simulation results show that compared with the existing compressed sensing procedures, our method can obviously improve the performance of reconstruction of signals with noise, and can be used to perform the front-end spectrum analysis of absorbing materials and better detect the active channels in cognitive radio.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2014年第20期139-146,共8页
Acta Physica Sinica
基金
国家科技重大专项(批准号:2008ZX03006)资助的课题~~
关键词
频谱感知
压缩感知
信号重构
选择性测量
spectrum sensing, compressed sensing, signal reconstruction, selective measure
