本文選題：壓縮感知(CS)　切入點(diǎn)：稀疏性　出處：《北京化工大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年05期 　論文類(lèi)型：期刊論文

【摘要】：為了解決壓縮感知(CS)重構(gòu)算法通過(guò)重構(gòu)稀疏系數(shù)求解原始信號(hào)的重構(gòu)精度不高的問(wèn)題,提出一種基于信號(hào)空間的壓縮采樣匹配追蹤算法。首先在冗余字典中求解原始信號(hào)的最優(yōu)表示空間,然后在最優(yōu)表示空間中利用迭代算法直接求解原始信號(hào),最后以軸承故障振動(dòng)信號(hào)為例進(jìn)行實(shí)驗(yàn)驗(yàn)證。結(jié)果證明本文算法提高了信號(hào)的重構(gòu)精度,可以為增強(qiáng)機(jī)械振動(dòng)信號(hào)的故障檢測(cè)能力提供依據(jù)。
[Abstract]:In order to solve the problem that the reconstruction accuracy of the original signal is not high by the reconstruction sparse coefficient, A compression sampling matching tracking algorithm based on signal space is proposed. Firstly, the optimal representation space of the original signal is solved in the redundant dictionary, and then the iterative algorithm is used to solve the original signal directly in the optimal representation space. Finally, the experimental results show that the proposed algorithm improves the reconstruction accuracy of the signal, and can provide the basis for enhancing the fault detection ability of the mechanical vibration signal.
【作者單位】： 北京化工大學(xué)機(jī)電工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(51405012)
【分類(lèi)號(hào)】：TH17
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本文編號(hào)：1573888