本文關(guān)鍵詞：基于1_p范數(shù)的壓縮感知重構(gòu)算法及應(yīng)用研究　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 壓縮感知 重構(gòu)算法 圖像重構(gòu) DOA估計

【摘要】：傳統(tǒng)Nyquist采樣定理指出：為了不失真的恢復(fù)模擬信號，采樣頻率應(yīng)該大于或等于模擬信號頻譜中最高頻率的兩倍。高采樣率會產(chǎn)生海量數(shù)據(jù)，其存儲和傳輸是一項艱難的工作，并且產(chǎn)生了大量的冗余數(shù)據(jù)，會造成資源浪費。近年來提出的壓縮感知理論指出：對稀疏或者可壓縮信號進行少量非自適應(yīng)線性投影，投影信號含有足夠的信息，從而能對信號進行高概率重建。壓縮感知理論的出現(xiàn)改變了先高速率采樣然后低碼率壓縮的信息采集模式，允許采樣和壓縮同時進行，并且只需采樣部分信息，極大地節(jié)省了系統(tǒng)資源。 本文首先介紹了壓縮感知理論，重點介紹了壓縮感知重構(gòu)算法。針對現(xiàn)有的算法應(yīng)用于圖像的重構(gòu)中時，重構(gòu)信噪比不高，尤其在與分塊思想集合，低采樣率時,塊效應(yīng)明顯這一缺點，本文提出一種新的基于l p(0p1)范數(shù)的將罰函數(shù)法與修正Hesse陣序列二次規(guī)劃方法結(jié)合的壓縮感知重構(gòu)算法。將提出的算法用于圖像重構(gòu),仿真實驗表明所提出的新算法可以提高圖像恢復(fù)精度，在低采樣率時，塊效應(yīng)減小，，重構(gòu)性能明顯優(yōu)于現(xiàn)有的算法。 為了更好地實現(xiàn)對壓縮感知的實際應(yīng)用，本文研究了基于壓縮感知的麥克風(fēng)陣列遠場聲源DOA估計模型。由于麥克風(fēng)陣列聲源DOA估計模型首先滿足了壓縮感知要求的稀疏性條件，其次，遠場聲源DOA估計模型中聲源到麥克風(fēng)陣列形成的觀測矩陣滿足壓縮感知測量矩陣的RIP條件。因此，理論上本文提出算法可以用于該模型中，仿真實驗也表明，本文提出的基于范數(shù)的壓縮感知重構(gòu)算法可以用于遠場DOA估計，并且取得了較好的結(jié)果。
[Abstract]:The traditional Nyquist sampling theorem points out that in order to recover the analog signal without distortion, the sampling frequency should be greater than or equal to twice of the highest frequency in the analog signal spectrum. It is a difficult task to store and transfer, and it produces a lot of redundant data. The theory of compression perception proposed in recent years points out that a small amount of non-adaptive linear projection is used for sparse or compressible signals, and the projection signals contain sufficient information. The theory of compression sensing changes the information acquisition mode of high rate sampling and then low bit rate compression, which allows sampling and compression to be carried out simultaneously, and only a part of the information needs to be sampled. The system resources are greatly saved. In this paper, we first introduce the theory of compression perception, and focus on the compression perception reconstruction algorithm. When the existing algorithms are used in image reconstruction, the SNR of reconstruction is not high, especially in the set of the idea of block. When the sampling rate is low, the block effect is obvious. In this paper, we propose a new compression perceptual reconstruction algorithm which combines penalty function method with modified Hesse array sequence quadratic programming method based on l p0 p1) norm. The proposed algorithm is used for image reconstruction. Simulation results show that the proposed algorithm can improve the accuracy of image restoration. At low sampling rate, the block effect is reduced, and the reconstruction performance is obviously better than the existing algorithm. In order to realize the practical application of compression perception better. In this paper, the far-field sound source DOA estimation model of microphone array based on compressed sensing is studied. Firstly, the DOA estimation model of microphone array satisfies the sparse condition of compression sensing, and secondly. The observation matrix formed from the sound source to microphone array in the far-field sound source DOA estimation model satisfies the RIP condition of the compressed sensing measurement matrix. Therefore, the algorithm proposed in this paper can be used in the model theoretically. The simulation results also show that the proposed algorithm can be used in far field DOA estimation, and good results are obtained.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

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