本文選題：壓縮傳感 + 無(wú)線傳感器網(wǎng)絡(luò)��； 參考：《南開(kāi)大學(xué)》2014年博士論文

【摘要】：作為物聯(lián)網(wǎng)的核心技術(shù),無(wú)線傳感器網(wǎng)絡(luò)(WSN)因?yàn)槠鋺?yīng)用靈活性和信息感知有效性受到了越來(lái)越多的關(guān)注。也正是因?yàn)橐ＷC傳感器節(jié)點(diǎn)的靈活性,其硬件資源和能源供給部分受到限制,難以滿足大規(guī)模高密度海量信息的傳送和處理,成為制約WSN大規(guī)模應(yīng)用的重大技術(shù)難題。 近年來(lái),壓縮傳感(CS)理論獲得了廣泛關(guān)注和研究。壓縮傳感將采樣與壓縮過(guò)程合并,直接將稀疏或者可壓縮信號(hào)中的“冗余”信息丟棄,因此降低了信號(hào)采樣頻率,并且節(jié)省了存儲(chǔ)和傳輸成本。壓縮傳感理論的出現(xiàn),為無(wú)線傳感器網(wǎng)絡(luò)的海量數(shù)據(jù)采集、傳輸、存儲(chǔ)以及節(jié)點(diǎn)續(xù)航能力等問(wèn)題提供了一種全新的技術(shù)解決方案,可加快物聯(lián)網(wǎng)工程發(fā)展的步伐。 本文在對(duì)無(wú)線傳感器網(wǎng)絡(luò)數(shù)據(jù)特性分析的基礎(chǔ)上,將壓縮傳感理論的關(guān)鍵技術(shù)作為主要研究?jī)?nèi)容,致力于將CS理論應(yīng)用到WSN中,為此展開(kāi)了多方面研究工作： 1.信號(hào)稀疏表示：在自然界中的稀疏信號(hào)是少見(jiàn)的,但是大部分信號(hào)都可以在某個(gè)域上進(jìn)行稀疏表示。在深入研究WSN數(shù)據(jù)特性的基礎(chǔ)上,利用過(guò)完備字典對(duì)信號(hào)進(jìn)行稀疏,分別生成了離散余弦基、Haai、Chirplet以及Db小波等過(guò)完備原子庫(kù),并進(jìn)行了多參量級(jí)聯(lián)過(guò)完備學(xué)習(xí)字典仿真,仿真結(jié)果表明：Chirplet和Db小波過(guò)完備字典稀疏效果優(yōu)于DCT過(guò)完備字典和Haar小波過(guò)完備字典,使用范圍也更廣；級(jí)聯(lián)過(guò)完備字典對(duì)于與先驗(yàn)?zāi)Ｐ拖嗨频男盘?hào)稀疏效果非常明顯。 2.測(cè)量矩陣：基于滿足約束等距性(RIP)這一條件,對(duì)高斯隨機(jī)測(cè)量矩陣、伯努利隨機(jī)測(cè)量矩陣和托普利茲以及循環(huán)測(cè)量矩陣等進(jìn)行了研究,并通過(guò)仿真對(duì)之進(jìn)行了對(duì)比分析。并在此基礎(chǔ)上,提出了一種易于硬件實(shí)現(xiàn)、存儲(chǔ)空間需求低的伯努利偽隨機(jī)循環(huán)矩陣。仿真結(jié)果表明：在測(cè)量數(shù)M滿足一定條件時(shí),伯努利偽隨機(jī)矩陣可以高精度實(shí)現(xiàn)信號(hào)的測(cè)量與重構(gòu)。 3.信號(hào)重構(gòu)：重構(gòu)算法是目前研究較深入而且成果較多的一項(xiàng)技術(shù),本文從重構(gòu)精度、速度以及成功率等方面對(duì)現(xiàn)有的重構(gòu)算法進(jìn)行了對(duì)比分析,在分析現(xiàn)有各種算法優(yōu)劣性的基礎(chǔ)之上,根據(jù)WSN的數(shù)據(jù)特性,提出了一種實(shí)用性更強(qiáng)、重構(gòu)精度更高、穩(wěn)定性和魯棒性更好的ITSAOMP重構(gòu)算法。實(shí)驗(yàn)結(jié)果表明：在測(cè)量矩陣滿足一定條件時(shí),借助于ITSAOMP算法,可以高概率、低失真地重構(gòu)原始信號(hào),并且具備較好的噪聲魯棒性。 4.將壓縮傳感和周期非均勻采樣有機(jī)結(jié)合：周期非均勻采樣是有效降低采樣頻率、提高采樣精度的一種方法,它利用多通道采樣系統(tǒng)對(duì)信號(hào)進(jìn)行采樣。根據(jù)非均勻采樣系統(tǒng)的特點(diǎn),利用聯(lián)合子空間理論將采樣和重構(gòu)過(guò)程轉(zhuǎn)化為矩陣或向量運(yùn)算,并借助CS理論,將稀疏信號(hào)重構(gòu)算法應(yīng)用到非均勻采樣系統(tǒng)中對(duì)信號(hào)進(jìn)行重構(gòu),仿真結(jié)果表明系統(tǒng)不僅很好地實(shí)現(xiàn)信號(hào)采樣與重構(gòu),而且大大降低了采樣頻率,提高了重構(gòu)精度。
[Abstract]:As the core technology of the Internet of things, Wireless Sensor Networks (WSNs) has attracted more and more attention for its flexibility in application and effectiveness of information perception. It is also because the flexibility of sensor nodes is guaranteed that the hardware resources and energy supply are limited, so it is difficult to meet the transmission and processing of large-scale high-density and magnanimous information. In recent years, the theory of compressed sensing (CSN) has received extensive attention and research. The compression sensor combines the sampling process with the compression process and directly discards the "redundant" information in the sparse or compressible signal, thus reducing the sampling frequency and saving the storage and transmission costs. The emergence of compressed sensing theory provides a new technical solution for mass data acquisition, transmission, storage and node endurance of wireless sensor networks. Based on the analysis of the data characteristics of wireless sensor networks, the key technology of compression sensing theory is taken as the main research content in this paper, and the CS theory is applied to WSN. To this end, a number of research work has been carried out: 1. Signal sparse representation: sparse signals are rare in nature, but most signals can be represented sparsely in a certain domain. On the basis of deeply studying the characteristics of WSN data, the over complete dictionary is used to sparse the signal, and the over complete atomic libraries such as the discrete cosine base Haaian Chirplet and the Db wavelet are generated, respectively, and the simulation of the multi parameter cascade over complete learning dictionary is carried out. The simulation results show that the sparse effect of the over complete dictionaries of the two wavelets is better than that of the over complete dictionaries of DCT and Haar wavelets, and the cascaded over complete dictionaries have obvious effect on the sparsity of signals similar to the prior models. 2. Measurement matrix: based on the condition of satisfying the constraint equidistant property, the Gao Si random measurement matrix, Bernoulli random measurement matrix, Topril matrix and cyclic measurement matrix are studied, and the simulation results are compared and analyzed. On this basis, a Bernoulli pseudorandom cyclic matrix is proposed, which is easy to implement in hardware and low in storage space. The simulation results show that the Bernoulli pseudorandom matrix can be used to measure and reconstruct the signal with high precision when the measurement number M satisfies certain conditions. Signal reconstruction: the reconstruction algorithm is a technology which has been studied deeply and has achieved a lot at present. In this paper, the existing reconstruction algorithms are compared and analyzed from the aspects of reconstruction precision, speed and success rate, etc. Based on the analysis of the advantages and disadvantages of the existing algorithms and according to the data characteristics of WSN, this paper proposes a more practical ITSAOMP algorithm with higher reconstruction accuracy and better stability and robustness. The experimental results show that the ITSAOMP algorithm can reconstruct the original signal with high probability and low distortion when the measurement matrix satisfies certain conditions. It combines compression sensing with periodic non-uniform sampling: periodic non-uniform sampling is a method to reduce sampling frequency and improve sampling precision effectively. It uses multi-channel sampling system to sample signals. According to the characteristics of non-uniform sampling system, the process of sampling and reconstruction is transformed into matrix or vector operation by using the theory of joint subspace, and the sparse signal reconstruction algorithm is applied to reconstruct the signal in the non-uniform sampling system with the help of CS theory. The simulation results show that the system not only realizes signal sampling and reconstruction, but also greatly reduces the sampling frequency and improves the reconstruction accuracy.
【學(xué)位授予單位】：南開(kāi)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN929.5;TP212.9

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 鄧琳;呂幼新;王洪;;并行采集系統(tǒng)通道失配誤差測(cè)量及校正[J];電子科技大學(xué)學(xué)報(bào);2006年03期

2 孫蒙;王正明;;兩類混合特征信號(hào)的超完備稀疏表示方法[J];電子學(xué)報(bào);2007年07期

3 練秋生;郝鵬鵬;;基于壓縮傳感和代數(shù)重建法的CT圖像重建[J];光學(xué)技術(shù);2009年03期

4 方紅;章權(quán)兵;韋穗;;改進(jìn)的后退型最優(yōu)正交匹配追蹤圖像重建方法[J];華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年08期

5 趙瑞珍;林婉娟;李浩;胡紹海;;基于光滑l_0范數(shù)和修正牛頓法的壓縮感知重建算法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2012年04期

6 李樹(shù)濤;魏丹;;壓縮傳感綜述[J];自動(dòng)化學(xué)報(bào);2009年11期

7 張成;章權(quán)兵;張芬;韋穗;;超稀疏三元循環(huán)測(cè)量矩陣的設(shè)計(jì)[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年10期

8 李林;孔令富;練秋生;;基于輪廓波維納濾波的圖像壓縮傳感重構(gòu)[J];儀器儀表學(xué)報(bào);2009年10期

9 張景雄;陽(yáng)柯;郭建中;;壓縮感知的信息論解譯[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2014年11期

相關(guān)博士學(xué)位論文 前2條

1 楊海蓉;壓縮傳感的測(cè)量矩陣與恢復(fù)算法研究[D];安徽大學(xué);2011年

2 趙小川;層次型無(wú)線傳感器網(wǎng)絡(luò)關(guān)鍵技術(shù)研究[D];北京郵電大學(xué);2013年

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本文編號(hào)：2006031