【摘要】：針對(duì)傳感器數(shù)目小于源信號(hào)數(shù)目的欠定情形,研究了基于壓縮感知(CS)的欠定盲源分離(UBSS)問題。從欠定盲源分離和壓縮感知的數(shù)學(xué)模型入手,在源信號(hào)具有稀疏性的前提下,將其轉(zhuǎn)化為CS理論中的稀疏信號(hào)重構(gòu)問題。在Sparco框架下建立了CS-UBSS兩步法算法通用模型,并理論證明了該模型的有限等距特性(RIP)。仿真結(jié)果說明了該算法模型針對(duì)語音信號(hào)和圖像信號(hào)的可行性與適用性,拓寬了UBSS問題的解決思路,尤其是CS理論中性能優(yōu)越的重構(gòu)算法可以直接應(yīng)用于源信號(hào)的恢復(fù)。
[Abstract]:In the case that the number of sensors is less than the number of source signals, the problem of blind source separation (UBSS) based on compressed sensing (CS) is studied. Based on the mathematical model of under-determined blind source separation and compression sensing, the sparse signal reconstruction problem in CS theory is transformed under the premise that the source signal is sparse. In this paper, a general model of CS-UBSS two-step algorithm is established under the framework of Sparco, and the finite isometric characteristic (RIP). Of the model is proved theoretically. The simulation results show the feasibility and applicability of this algorithm model for speech signal and image signal, and widen the idea of solving UBSS problem. Especially, the reconstruction algorithm with superior performance in CS theory can be directly applied to the restoration of source signal.
【作者單位】： 景德鎮(zhèn)陶瓷大學(xué)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金(51377132)
【分類號(hào)】：TN911.7
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本文編號(hào)：2446782