本文選題：結(jié)構(gòu)振動(dòng)壓縮采樣信號(hào)　切入點(diǎn)：分布式壓縮感知　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：壓縮感知(Compressive Sensing,CS)是20世紀(jì)初發(fā)展起來(lái)的在信號(hào)處理領(lǐng)域具有顛覆性的一種理論,越來(lái)越多學(xué)科領(lǐng)域的專家學(xué)者開(kāi)始致力于CS的研究。將土木工程和壓縮感知結(jié)合,勢(shì)必也會(huì)有廣闊的應(yīng)用前景。分布式壓縮感知(Distributed Compressive Sensing,DCS)在CS的基礎(chǔ)上研究多個(gè)通道信號(hào)之間的相關(guān)性,發(fā)展了三種不同的聯(lián)合稀疏模型,進(jìn)一步降低了采樣率。而多觀測(cè)向量(Multiple Measurement Vectors,MMV)問(wèn)題被稱作聯(lián)合稀疏求解問(wèn)題,是另外一種研究信號(hào)間相關(guān)性的框架,本質(zhì)與JSM2一致。本文的研究目的是研究適用于多通道結(jié)構(gòu)振動(dòng)信號(hào)的聯(lián)合稀疏模型并且發(fā)展相應(yīng)的壓縮信號(hào)恢復(fù)算法。主要內(nèi)容包含以下三個(gè)方面。本文介紹了分布式壓縮感知、多觀測(cè)向量的框架以及相應(yīng)的聯(lián)合恢復(fù)算法。針對(duì)DCS的三種聯(lián)合稀疏模型,分別選擇SiOMP、DCSSOMP、Texas DOI恢復(fù)算法進(jìn)行研究,驗(yàn)證聯(lián)合恢復(fù)相較于單通道信號(hào)的優(yōu)勢(shì)。然后結(jié)合實(shí)際的多通道結(jié)構(gòu)振動(dòng)信號(hào)的特點(diǎn),確定第二種聯(lián)合稀疏模型(Joint Sparsity Model 2,JSM2)作為適用于多通道結(jié)構(gòu)振動(dòng)壓縮感知信號(hào)的模型。JSM2所用的分布式壓縮感知同步正交匹配追蹤(DCS Simultaneously Orthogonal Matching Pursuit,DCSSOMP)算法是一種經(jīng)典的聯(lián)合恢復(fù)算法。本文改變了DCSSOMP算法的原子選擇公式從而大幅節(jié)約運(yùn)算的時(shí)間,然后結(jié)合下采樣(sub-sampling)以及AIC(Analog to Information Converter)采樣,將改進(jìn)的DCSSOMP算法應(yīng)用于實(shí)際的結(jié)構(gòu)壓縮采樣信號(hào)的聯(lián)合恢復(fù)當(dāng)中,驗(yàn)證了該稀疏模型和恢復(fù)算法對(duì)振動(dòng)信號(hào)聯(lián)合恢復(fù)可行性。文章驗(yàn)證了同步下采樣和非同步下采樣恢復(fù)效果是一致的,而AIC采樣則在聯(lián)合重構(gòu)效果上略遜于下采樣。本文最后研究了多觀測(cè)向量問(wèn)題中的比較新穎的基于增廣子空間的多重信號(hào)分類(Subspace Augmented Multiple Signal Classification,SAMUSIC)算法,對(duì)比了SAMUSIC算法以及DCSSOMP算法的性能。證明了在無(wú)噪聲且當(dāng)信號(hào)個(gè)數(shù)比較多時(shí),SAMUSIC算法是遠(yuǎn)優(yōu)于DCSSOMP算法的,而在信號(hào)噪聲比較大的情況下,SAMUSIC算法與DCSSOMP算法的恢復(fù)性能一致,同時(shí)也分析了兩者的時(shí)間復(fù)雜度。最后將SAMUISC算法應(yīng)用到實(shí)際結(jié)構(gòu)壓縮采樣信號(hào)的聯(lián)合重構(gòu)中。
[Abstract]:Compressed-sensing (CSC) is a subversive theory in the field of signal processing developed in the early 20th century. More and more experts and scholars in more and more disciplines are beginning to devote themselves to the research of CS. Distributed compressed sensing Compressive sensing (DCS) studies the correlation between multiple channel signals based on CS, and develops three different joint sparse models. The multi-observation vector multiple Measurement VectorsMVD problem is called the joint sparse solution problem, which is another framework to study the correlation between signals. The purpose of this paper is to study the joint sparse model for multi-channel structure vibration signal and develop the corresponding compression signal recovery algorithm. The main contents include the following three aspects. Distributed compression awareness, The frame of multi-observation vector and the corresponding joint recovery algorithm. For the three joint sparse models of DCS, we select the SiOMPI DCS SSOMP DOI recovery algorithm to study. Verify the advantages of joint recovery over single channel signals. Then combine the characteristics of actual multi-channel structural vibration signals, Determine the second joint sparse model Sparsity Model 2 JSM2 as a model for vibration compression sensing of multi-channel structures .JSM2. The distributed compressed sensing synchronous orthogonal matching tracking (DCS Simultaneously Orthogonal Matching pursuit DCSSOMP) algorithm is a classic combination. In this paper, the atomic selection formula of DCSSOMP algorithm is changed to save the time of operation. Then, combining the sub-sampling and AIC(Analog to Information sampling, the improved DCSSOMP algorithm is applied to the joint recovery of the actual compressed sampling signals. It is proved that the sparse model and the restoration algorithm are feasible for the joint restoration of vibration signals. The effect of synchronous down-sampling and non-synchronous down-sampling recovery is proved to be consistent in this paper. AIC sampling is slightly inferior to lower sampling in joint reconstruction. Finally, a novel subspace Augmented Multiple Signal classification algorithm based on augmented subspace for multi-observation vector problem is studied in this paper. The performances of SAMUSIC algorithm and DCSSOMP algorithm are compared. It is proved that the SAMUSIC algorithm is much better than the DCSSOMP algorithm when there is no noise and when the number of signals is large, and the recovery performance of SAMUSIC algorithm is the same as that of DCSSOMP algorithm under the condition of high signal noise. At the same time, the time complexity of the two methods is analyzed. Finally, the SAMUISC algorithm is applied to the joint reconstruction of compressed sampling signals with actual structure.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TU317

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本文編號(hào)：1700042