本文選題：水下回波信號 + 先驗(yàn)信息�。� 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：水下目標(biāo)回波信號是主動(dòng)聲吶探測和識別的基礎(chǔ)。水下環(huán)境復(fù)雜多變,存在各種噪聲和干擾,尤其隨著隱身技術(shù)的發(fā)展和各種小目標(biāo)的出現(xiàn),使得目標(biāo)回波信號越來越弱,因此,弱回波信號檢測和處理問題是水聲信號處理領(lǐng)域研究的熱點(diǎn)和難點(diǎn)問題。另外,為了提高系統(tǒng)的探測精度,增加抗干擾能力,提高目標(biāo)檢測概率等,需要不斷增加信號帶寬。然而帶寬的增加使得數(shù)據(jù)量急劇增大,給信號的采集、存儲(chǔ)、傳輸和處理等帶來極大的負(fù)擔(dān)。在這種背景下,本文對壓縮感知在水下回波信號處理中的應(yīng)用展開研究,擬解決目前水下回波信號處理中存在的弱信號檢測和采樣數(shù)據(jù)量過大等難題,主要內(nèi)容有:首先,闡述了本論文的研究背景和意義,并對壓縮感知理論以及其在水下信號處理中的應(yīng)用進(jìn)行了國內(nèi)外研究現(xiàn)狀綜述,給出了本論文的研究思路和結(jié)構(gòu)安排。其次,以壓縮感知理論為基礎(chǔ),重點(diǎn)研究了幾種常見的稀疏基、測量矩陣、匹配追蹤的重構(gòu)算法以及相應(yīng)的改進(jìn)算法,給出了信號重構(gòu)效果測衡量標(biāo)準(zhǔn),并從水下回波信號基本理論出發(fā),采用亮點(diǎn)模型實(shí)現(xiàn)了水下回波信號的仿真。在此基礎(chǔ)上,通過仿真實(shí)驗(yàn),比較了不同稀疏基、不同測量矩陣及不同重構(gòu)算法對水下回波信號處理結(jié)果的影響,提出基于離散余弦稀疏基、高斯隨機(jī)矩陣和分段正交匹配追蹤算法的壓縮感知處理框架。再次,充分分析水下回波信號的形成原理和結(jié)構(gòu)特性,研究回波信號與入射信號的關(guān)系、回波信號的分塊特性等,將入射信號和塊稀疏特性作為先驗(yàn)信息,融入稀疏分解和重構(gòu)過程,提出融入先驗(yàn)信息的壓縮感知處理方法。并進(jìn)一步應(yīng)用到水下回波信號處理中,采用信噪比的提高量、匹配度、相對誤差等指標(biāo)衡量了該方法的處理效果。仿真實(shí)驗(yàn)結(jié)果充分顯示了該方法在提高信噪比和減少數(shù)據(jù)量方面的優(yōu)勢。最后,總結(jié)了本文的主要工作和創(chuàng)新,并對下一步應(yīng)展開的研究進(jìn)行了展望。
[Abstract]:Underwater target echo signal is the basis of active sonar detection and recognition. The underwater environment is complex and changeable, there are various noises and disturbances, especially with the development of stealth technology and the appearance of various small targets, the echo signal of the target becomes weaker and weaker. Weak echo signal detection and processing is a hot and difficult problem in the field of underwater acoustic signal processing. In addition, in order to improve the detection accuracy of the system, increase the ability of anti-jamming and improve the probability of target detection, it is necessary to continuously increase the signal bandwidth. However, the increase of bandwidth makes the amount of data increase rapidly, which brings great burden to signal acquisition, storage, transmission and processing. In this context, the application of compression sensing in underwater echo signal processing is studied in this paper, and the problems of weak signal detection and excessive sampling data in underwater echo signal processing are solved. The main contents are as follows: first of all, The research background and significance of this paper are expounded, and the theory of compression sensing and its application in underwater signal processing are summarized at home and abroad, and the research ideas and structure of this paper are given. Secondly, based on the theory of compression perception, several common sparse bases, measurement matrices, matching tracking reconstruction algorithms and corresponding improved algorithms are studied, and the measurement criteria for signal reconstruction effect are given. Based on the basic theory of underwater echo signal, the simulation of underwater echo signal is realized by using bright spot model. On this basis, the effects of different sparse bases, different measurement matrices and different reconstruction algorithms on the underwater echo signal processing results are compared through simulation experiments, and a discrete cosine sparse basis is proposed. Gao Si random matrix and piecewise orthogonal matching tracking algorithm compression perception processing framework. Thirdly, the formation principle and structure characteristic of underwater echo signal are analyzed fully, the relation between echo signal and incident signal, the block characteristic of echo signal are studied, and the incident signal and block sparse characteristic are taken as prior information. In the process of sparse decomposition and reconstruction, this paper proposes a method of processing compression perception with prior information. It is further applied to underwater echo signal processing. The improvement of signal-to-noise ratio, matching degree and relative error are used to evaluate the processing effect of the method. The simulation results show the advantages of this method in improving signal-to-noise ratio and reducing the amount of data. Finally, the main work and innovation of this paper are summarized, and the future research is prospected.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TB56;TN911.7

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