DPSS基下多带流信号的恢复  

The Recovery of Multi-band Signals Based on Discrete Prolate Spheroidal Sequence

作　　者：李新慧[1] 朱燊 郭文彬[1] 

机构地区：[1]北京邮电大学泛网无线通信教育部重点实验室,北京100876

出　　处：《信号处理》2017年第4期595-600,共6页Journal of Signal Processing

基　　金：国家自然基金(NSFC 61271181);教育部国家重点实验室开放基金(Grant 9140C170203140C17084)

摘　　要：压缩感知技术是近年来信号处理领域最热门的技术。传统的压缩感知理论并未考虑到稀疏信号本身可能具有的某种结构,块稀疏就是其中的一种。本文针对压缩感知的多带块稀疏流信号,将稀疏信号重构算法与调制的DPSS(Discrete Prolate Spheroidal Sequence)基扩展相结合,建立了多带块稀疏模型,并利用压缩感知AIC结构,在远低于奈奎斯特速率下对多带模拟信号进行采样。结合压缩感知获得的观测方程和利用前后窗内信号的相关性建立信号状态转移方程,采用降阶的卡尔曼滤波算法恢复原始信号。相对于傅里叶基扩展,DPSS基扩展在降低采样结构复杂度的同时,克服了频谱泄露的问题。仿真结果表明,多带信号在DPSS基下的恢复性能优于多带信号在FFT基下的重构。Recent years, Compressed Sensing is one of the most popular technology in signal processing fields. However, the traditional theory of CS leaves certain structure of sparse signals out of consideration, such as, block sparse. Aimed at multi-band block sparse streaming signals in compressed sensing, we combined sparse signals＇ reconstitution algorithm and modulated Discrete Prolate Spheroidal Sequence （DPSS） to establish the multi-band block sparse model. Then we use the correlations between the signals of two continuous windows to model the process in the state-space form so the original sig- nals can be regained with Kalman filter. Compared with Fourier-based, DPSS-based reduces the complexity of sampled structure and settles spectrum leakage. The simulation reveals that multi-band signals can achieve much better restorability in DPSS basis than Fourier basis.

关 键 词：压缩感知 流信号 DPSS 卡尔曼滤波 多频带信号 

分 类 号：TN911[电子电信—通信与信息系统]