本文選題：壓縮感知（CS） + 隨機解調(diào)(RD)�。� 參考：《哈爾濱工業(yè)大學》2014年碩士論文

【摘要】：傳統(tǒng)的信號處理模式是先進行奈奎斯特采樣，再通過壓縮去除大量冗余數(shù)據(jù)。這種是先采樣后壓縮的模式無疑浪費了大量的資源。模擬信息轉(zhuǎn)換是解決這個弊端的一種思路，它旨在利用一些新的信息理論將模擬信號直接轉(zhuǎn)變?yōu)橛杏玫男畔�，從而減少采樣過程中的冗余數(shù)據(jù)，降低采樣率。壓縮感知是實現(xiàn)模擬信息轉(zhuǎn)換的一種重要理論依據(jù)，它在理論上表明利用信號的稀疏性可以實現(xiàn)信號的同時壓縮和采樣，達到將模擬信號直接轉(zhuǎn)變?yōu)閴嚎s過的有用信息的目的，即模擬信息轉(zhuǎn)換。但是壓縮感知的物理實現(xiàn)方法目前還很少，隨機解調(diào)是其中一種重要方法和技術(shù)。本文對此開展研究，采用隨機解調(diào)方式使壓縮感知理論實用化，從而實現(xiàn)模擬信息轉(zhuǎn)換。本文的主要研究內(nèi)容和結(jié)果如下： 1、研究隨機解調(diào)的原理。對壓縮感知理論和隨機解調(diào)原理進行闡述，用數(shù)學語言描述隨機解調(diào)過程的各個階段，說明隨機解調(diào)是壓縮感知從離散域到連續(xù)域的一種擴展。通過MATLAB仿真驗證隨機解調(diào)技術(shù)的可行性，并探究了采樣速率、采樣相位、濾波器參數(shù)、m序列周期等若干因素的影響作用，為后續(xù)隨機解調(diào)物理系統(tǒng)的設計提供參考。 2、設計隨機解調(diào)實驗平臺。以隨機解調(diào)技術(shù)的理論研究及仿真結(jié)果為指導，設計隨機解調(diào)物理系統(tǒng)作為實驗平臺。該系統(tǒng)包括硬件和軟件兩個部分。硬件部分設計了信號調(diào)理板卡，整合了多種PXIe儀器設備，利用先進的PXIe測試總線進行設備互連，完成信號的產(chǎn)生、混頻、濾波、放大和采樣任務，以及數(shù)據(jù)存儲和傳輸任務。采用LabVIEW語言開發(fā)軟件，實現(xiàn)對各個硬件模塊的參數(shù)配置和靈活控制，完成信號顯示分析、算法執(zhí)行、報表生成等任務。 3、構(gòu)造隨機解調(diào)系統(tǒng)感知矩陣。感知矩陣是信號重構(gòu)階段的重要參數(shù)，它包含了系統(tǒng)的重要特性，它的準確程度與信號重構(gòu)效果緊密相關。本文研究了感知矩陣的理論計算法，即根據(jù)系統(tǒng)各部分的理論模型、參數(shù)、表達式計算系統(tǒng)的感知矩陣的方法。然后提出了兩種效果更好的方法：步進頻率激勵法，基于m序列和FFT的快速構(gòu)造法。步進正弦激勵法是將被測信號用一系列頻率步進的正弦和余弦信號代替，利用一系列對應的系統(tǒng)輸出信號的采樣值構(gòu)造感知矩陣�；趍序列和FFT的快速構(gòu)造法首先采用m序列作為激勵信號獲得隨機解調(diào)系統(tǒng)中模擬乘法器、低通濾波器和運放三部分電路整體的脈沖響應；然后用獲得的脈沖響應、m序列計算觀測矩陣；最后對觀測矩陣的共軛轉(zhuǎn)置矩陣進行FFT，之后將FFT結(jié)果再次共軛轉(zhuǎn)置得到感知矩陣�？焖贅�(gòu)造法相比前兩種方法能夠獲得準確度和計算效率的同時提高，是本文實驗中所采用的方法。 4、利用隨機解調(diào)系統(tǒng)進行硬件實驗。大量實驗表明可以利用本文設計隨機解調(diào)系統(tǒng)可以實現(xiàn)對50kHz以內(nèi)的多諧波信號的壓縮采樣，采樣率僅為4kS/s，遠小于信號的奈奎斯特采樣率，即壓縮比可達4%，，信噪比可達15dB以上。另外通過硬件實驗還探究了重構(gòu)信號的信噪比與采樣相位偏差程度、信號稀疏度的關系。
[Abstract]:The traditional signal processing mode is to carry out Nyquist sampling first and then to remove a large amount of redundant data through compression. This is the mode of pre sampling and compression. No doubt a lot of resources are wasted. Analog information conversion is a way of thinking to solve this problem. It aims to make use of some new information theory to transform the analog signal directly into useful. In order to reduce the redundant data in the sampling process and reduce the sampling rate, compression perception is an important theoretical basis for the realization of analog information conversion. In theory, it shows that using the sparsity of the signal can compress and sample the signal at the same time, and achieve the purpose of transforming the analog signal directly into the compressed useful information, that is, the model. However, there are few physical implementations of compressed sensing. Random demodulation is one of the most important methods and techniques. In this paper, the theory of random demodulation is used to make the compression perception theory practical and thus the analog information conversion is realized. The main research content and results of this paper are as follows:
1, the principle of random demodulation is studied. The theory of compressed sensing and the principle of random demodulation are expounded. The various stages of the random demodulation process are described in mathematical language. It shows that the random demodulation is an extension of the compression perception from the discrete domain to the continuous domain. The feasibility of the random demodulation is verified by MATLAB simulation, and the sampling rate and sampling are explored. The influence of some factors such as phase, filter parameters, m sequence period and so on will provide a reference for the design of subsequent random demodulation physical system.
2, a random demodulation experimental platform is designed. Based on the theoretical research and simulation results of random demodulation technology, a random demodulation physical system is designed as an experimental platform. The system includes two parts of hardware and software. The hardware part designs a signal conditioning board, integrates a variety of PXIe devices, and uses an advanced PXIe test bus to set up the system. Interconnect, complete the signal generation, frequency mixing, filtering, amplification and sampling tasks, and data storage and transmission tasks. Using LabVIEW language development software to realize the parameters configuration and flexible control of each hardware module, complete the signal display analysis, algorithm execution, report generation and other tasks.
3, the perceptual matrix of the stochastic demodulation system is constructed. The perceptual matrix is an important parameter in the phase of signal reconstruction. It contains the important characteristics of the system and its accuracy is closely related to the effect of the signal reconstruction. This paper studies the theoretical calculation method of the perceptual matrix, namely, the perception of the system based on the theoretical models, parameters and expressions of the system parts. Two better methods are then proposed: step frequency excitation method, fast construction method based on m sequence and FFT. Step sine excitation method is the substitution of sinusoidal and cosine signals with a series of frequency step signals, and a series of corresponding system output signals are used to construct the perception matrix. Based on m sequence, the step sine excitation method is used to construct the sensing matrix. The fast construction method of column and FFT first uses the m sequence as the excitation signal to obtain the impulse response of the analog multiplier, the low pass filter and the three part of the amplifier in the random demodulation system; then the observed matrix is calculated by the acquired pulse response and the m sequence; finally, the conjugate transposed matrix of the observation matrix is FFT, and then the FFT result is obtained. It is a method used in this experiment to improve the accuracy and calculation efficiency compared with the first two methods.
4, using the random demodulation system to carry out the hardware experiment. A large number of experiments show that the random demodulation system designed in this paper can realize the compression sampling of the multi harmonic signals within 50kHz. The sampling rate is only 4kS/s, which is far less than the Nyquist sampling rate of the signal, that is, the compression ratio can reach 4% and the signal to noise ratio can reach 15dB. The relationship between the SNR of reconstructed signal and the degree of sampling phase deviation and the degree of signal sparsity is also explored.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.3

【參考文獻】

相關期刊論文 前10條

1 石光明;劉丹華;高大化;劉哲;林杰;王良君;;壓縮感知理論及其研究進展[J];電子學報;2009年05期

2 焦李成;楊淑媛;劉芳;侯彪;;壓縮感知回顧與展望[J];電子學報;2011年07期

3 劉郁林;張先玉;王銳華;吳麗云;;頻域全通式壓縮感知隨機濾波器設計[J];重慶大學學報;2011年10期

4 許海濤;陳彪;呂志高;;UWB的調(diào)制技術(shù)及其應用前景[J];信息技術(shù);2011年04期

5 周瑞;;基于壓縮傳感的隨機濾波器設計[J];科技信息;2011年06期

6 李樹濤;魏丹;;壓縮傳感綜述[J];自動化學報;2009年11期

7 黃翔東;李文元;王兆華;;基于快速m序列變換的線性網(wǎng)絡沖激響應測量算法[J];數(shù)據(jù)采集與處理;2006年02期

8 張新躍,沈樹群;UWB超寬帶無線通信技術(shù)及其發(fā)展前景[J];數(shù)據(jù)通信;2004年02期

9 李平友;用最長序列測量揚聲器的脈沖響應[J];應用聲學;1993年01期

10 曾浩;潘卉青;葉們;王厚軍;;高速非均勻采樣信號的重構(gòu)方法[J];自動化儀表;2010年09期

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