本文選題：壓縮感知 + 稀疏時(shí)變信號(hào)��； 參考：《南京理工大學(xué)》2014年碩士論文

【摘要】：壓縮感知是從信號(hào)稀疏表示和函數(shù)逼近理論發(fā)展形成的信號(hào)低速率采樣理論。它以稀疏信號(hào)為研究對(duì)象,通過隨機(jī)線性映射將稀疏信號(hào)投影到低維空間實(shí)現(xiàn)信號(hào)的低速采樣。信號(hào)重構(gòu)則通過稀疏優(yōu)化算法獲得。信號(hào)的稀疏性是應(yīng)用壓縮感知理論獲取低速采樣的前提。 傳統(tǒng)壓縮感知理論研究的稀疏信號(hào)是非時(shí)變的。但是,在雷達(dá)、通信和導(dǎo)航等實(shí)際應(yīng)用中,信號(hào)的稀疏性通常是隨時(shí)間變化的。因此研究稀疏時(shí)變信號(hào)的壓縮感知具有重要的實(shí)際意義。本文以脈沖雷達(dá)為應(yīng)用背景研究稀疏時(shí)變信號(hào)壓縮感知重構(gòu)算法。根據(jù)脈沖雷達(dá)回波信號(hào)的時(shí)變特征,建立稀疏時(shí)變信號(hào)模型,發(fā)展基于迭代重加權(quán)的稀疏時(shí)變信號(hào)重構(gòu)算法。在此基礎(chǔ)上,以正交壓縮采樣系統(tǒng)為例,對(duì)脈沖雷達(dá)回波信號(hào)的壓縮感知和動(dòng)態(tài)重構(gòu)問題進(jìn)行研究。本文的主要工作如下： 1.簡(jiǎn)述壓縮感知和信號(hào)稀疏表示的基本理論。首先簡(jiǎn)要介紹信號(hào)的稀疏表示、信號(hào)的壓縮測(cè)量及信號(hào)重構(gòu)問題；然后,對(duì)主要的壓縮感知重構(gòu)算法進(jìn)行了分類總結(jié),對(duì)其中與本文工作密切相關(guān)的迭代重加權(quán)算法進(jìn)行了詳細(xì)介紹；最后通過仿真實(shí)驗(yàn)對(duì)幾種典型的稀疏信號(hào)重構(gòu)算法進(jìn)行了性能比較。 2.發(fā)展稀疏時(shí)變信號(hào)重構(gòu)算法。本文提出將稀疏信號(hào)重構(gòu)中的迭代重加權(quán)思想應(yīng)用于重構(gòu)稀疏時(shí)變信號(hào),使用加權(quán)的方式將信號(hào)先驗(yàn)信息融入重構(gòu)過程中以跟蹤信號(hào)稀疏性的變化,發(fā)展了倒數(shù)加權(quán)l(xiāng)1范數(shù)最小化算法(RWL1)和多次倒數(shù)加權(quán)l(xiāng)1范數(shù)最小化算法(M-RWL1)。仿真分析了時(shí)域稀疏時(shí)變信號(hào)的重構(gòu)性能,結(jié)果表明,本文提出的RWL1和M-RWL1算法可以高精度重構(gòu)稀疏時(shí)變信號(hào),從而驗(yàn)證了本文所提出的迭代重加權(quán)策略對(duì)稀疏時(shí)變信號(hào)重構(gòu)是有效的。相比RWL1算法,M-RWL1算法由于采用了多次循環(huán)迭代的策略可獲得更好的重構(gòu)性能。 3.研究脈沖雷達(dá)回波信號(hào)的壓縮感知重構(gòu)問題。采用正交壓縮采樣系統(tǒng)獲取脈沖雷達(dá)回波信號(hào)的壓縮測(cè)量,將本文所提出的RWL1和M-RWL1算法用于重構(gòu)脈沖雷達(dá)回波信號(hào)。仿真實(shí)驗(yàn)的結(jié)果表明,本文提出的稀疏時(shí)變信號(hào)重構(gòu)算法可有效實(shí)現(xiàn)脈沖雷達(dá)回波信號(hào)的動(dòng)態(tài)重構(gòu)。
[Abstract]:Compression sensing is a low rate sampling theory developed from signal sparse representation and function approximation theory.The sparse signal is used as the research object and the sparse signal is projected to the low dimensional space by the random linear mapping to realize the low speed sampling of the signal.Signal reconstruction is obtained by sparse optimization algorithm.The sparsity of signal is the premise of using compression sensing theory to obtain low-speed sampling.The sparse signals studied by traditional compression sensing theory are non-time-varying.However, in radar, communication and navigation applications, the sparsity of signals usually varies with time.Therefore, it is of great practical significance to study the compressed perception of sparse time-varying signals.In this paper, the sparse time-varying signal compression perception reconstruction algorithm is studied in the background of pulse radar.According to the time-varying characteristics of pulse radar echo signal, a sparse time-varying signal model is established, and an iterative reweighted sparse time-varying signal reconstruction algorithm is developed.Taking orthogonal compression sampling system as an example, the compression sensing and dynamic reconstruction of pulse radar echo signal are studied.The main work of this paper is as follows:1.The basic theory of compressed sensing and sparse representation of signals is briefly described.Firstly, the sparse representation of signal, the compression measurement of signal and the problem of signal reconstruction are briefly introduced, and then the main algorithms of compression perception reconstruction are classified and summarized.The iterative reweighting algorithm, which is closely related to the work in this paper, is introduced in detail, and the performance of several typical sparse signal reconstruction algorithms is compared by simulation experiments.2.A sparse time-varying signal reconstruction algorithm is developed.In this paper, the idea of iterative reweighting in sparse signal reconstruction is applied to reconstruct sparse time-varying signal, and the prior information of signal is incorporated into the reconstruction process to track the change of signal sparsity.The inverse weighted l 1 norm minimization algorithm (RWL 1) and the multiple reciprocal weighted l 1 norm minimization algorithm (M RWL 1) are developed.Simulation results show that the proposed RWL1 and M-RWL1 algorithms can reconstruct sparse time-varying signals with high accuracy.It is proved that the iterative reweighting strategy proposed in this paper is effective for sparse time-varying signal reconstruction.Compared with RWL1 algorithm, M-RWL1 algorithm can achieve better reconstruction performance because of the strategy of multiple iterations.3.The problem of compression sensing reconstruction of pulse radar echo signal is studied.The compression measurement of pulse radar echo signal is obtained by orthogonal compression sampling system. The RWL1 and M-RWL1 algorithms proposed in this paper are used to reconstruct the pulse radar echo signal.The simulation results show that the sparse time-varying signal reconstruction algorithm proposed in this paper can effectively realize the dynamic reconstruction of pulse radar echo signal.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.51

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鐘鵬飛;譚浩;;一種運(yùn)行時(shí)可重構(gòu)的SCA規(guī)范兼容擴(kuò)展方案[J];微計(jì)算機(jī)信息;2009年03期

2 雷清,高宇,葉宏;航電系統(tǒng)的處理機(jī)重構(gòu)算法與實(shí)現(xiàn)[J];航空計(jì)算技術(shù);2001年01期

3 傅健 ,路宏年,邵軍明;旋轉(zhuǎn)中心隨旋轉(zhuǎn)角度變化的扇束重構(gòu)算法[J];航空制造技術(shù);2003年02期

4 王孝偉;李鐵才;何杰;;一種基于雙行線形傳感器的指紋重構(gòu)算法[J];計(jì)算機(jī)測(cè)量與控制;2007年10期

5 林俊杉;陳文斌;程晉;王立峰;;重構(gòu)電導(dǎo)率間斷界面的一種水平集方法[J];中國(guó)科學(xué)(A輯:數(shù)學(xué));2009年02期

6 張宗念;李金徽;黃仁泰;;迭代硬閾值壓縮感知重構(gòu)算法——IIHT[J];計(jì)算機(jī)應(yīng)用;2011年08期

7 王樹梅;趙衛(wèi)東;王志成;;一種基于對(duì)象的二值圖像重構(gòu)算法[J];計(jì)算機(jī)工程與應(yīng)用;2008年04期

8 孟U，

本文編號(hào)：1733402