本文選題：壓縮傳感 + 無線傳感器網(wǎng)絡��； 參考：《南開大學》2014年博士論文

【摘要】：作為物聯(lián)網(wǎng)的核心技術,無線傳感器網(wǎng)絡(WSN)因為其應用靈活性和信息感知有效性受到了越來越多的關注。也正是因為要保證傳感器節(jié)點的靈活性,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)模應用的重大技術難題。 近年來,壓縮傳感(CS)理論獲得了廣泛關注和研究。壓縮傳感將采樣與壓縮過程合并,直接將稀疏或者可壓縮信號中的“冗余”信息丟棄,因此降低了信號采樣頻率,并且節(jié)省了存儲和傳輸成本。壓縮傳感理論的出現(xiàn),為無線傳感器網(wǎng)絡的海量數(shù)據(jù)采集、傳輸、存儲以及節(jié)點續(xù)航能力等問題提供了一種全新的技術解決方案,可加快物聯(lián)網(wǎng)工程發(fā)展的步伐。 本文在對無線傳感器網(wǎng)絡數(shù)據(jù)特性分析的基礎上,將壓縮傳感理論的關鍵技術作為主要研究內容,致力于將CS理論應用到WSN中,為此展開了多方面研究工作： 1.信號稀疏表示：在自然界中的稀疏信號是少見的,但是大部分信號都可以在某個域上進行稀疏表示。在深入研究WSN數(shù)據(jù)特性的基礎上,利用過完備字典對信號進行稀疏,分別生成了離散余弦基、Haai、Chirplet以及Db小波等過完備原子庫,并進行了多參量級聯(lián)過完備學習字典仿真,仿真結果表明：Chirplet和Db小波過完備字典稀疏效果優(yōu)于DCT過完備字典和Haar小波過完備字典,使用范圍也更廣；級聯(lián)過完備字典對于與先驗模型相似的信號稀疏效果非常明顯。 2.測量矩陣：基于滿足約束等距性(RIP)這一條件,對高斯隨機測量矩陣、伯努利隨機測量矩陣和托普利茲以及循環(huán)測量矩陣等進行了研究,并通過仿真對之進行了對比分析。并在此基礎上,提出了一種易于硬件實現(xiàn)、存儲空間需求低的伯努利偽隨機循環(huán)矩陣。仿真結果表明：在測量數(shù)M滿足一定條件時,伯努利偽隨機矩陣可以高精度實現(xiàn)信號的測量與重構。 3.信號重構：重構算法是目前研究較深入而且成果較多的一項技術,本文從重構精度、速度以及成功率等方面對現(xiàn)有的重構算法進行了對比分析,在分析現(xiàn)有各種算法優(yōu)劣性的基礎之上,根據(jù)WSN的數(shù)據(jù)特性,提出了一種實用性更強、重構精度更高、穩(wěn)定性和魯棒性更好的ITSAOMP重構算法。實驗結果表明：在測量矩陣滿足一定條件時,借助于ITSAOMP算法,可以高概率、低失真地重構原始信號,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結合：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)對信號進行采樣。根據(jù)非均勻采樣系統(tǒng)的特點,利用聯(lián)合子空間理論將采樣和重構過程轉化為矩陣或向量運算,并借助CS理論,將稀疏信號重構算法應用到非均勻采樣系統(tǒng)中對信號進行重構,仿真結果表明系統(tǒng)不僅很好地實現(xiàn)信號采樣與重構,而且大大降低了采樣頻率,提高了重構精度。
[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.
【學位授予單位】：南開大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN929.5;TP212.9

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