本文選題：壓縮感知 + 稀疏重構(gòu)　； 參考：《江蘇科技大學》2017年碩士論文

【摘要】：隨著傳感器和陣列技術(shù)應用的逐漸增多,陣列信號的處理成為近年的熱點。波達方向(DOA,Direction of Arrival)估計作為陣列信號處理的一部分,經(jīng)過近幾十年的發(fā)展,其理論已日漸成熟,并廣泛應用于聲吶、通信、雷達、醫(yī)學等眾多領(lǐng)域。由于環(huán)境、硬件、能量供應等各方面的限制,現(xiàn)有的水聲陣列DOA估計方法在實際應用中往往會出現(xiàn)運算慢,精度不高等問題。已有的DOA估計方法大多是基于均勻陣列的基礎(chǔ)上進行研究的,通過擴大陣列的孔徑達到提高空間譜估計方法測角分辨率的目的,會使工程成本的急劇增加。所以在不增加方位估計成本的前提下,提出一種新型陣列模型具有經(jīng)濟型的意義。本文從水聲陣列信號的陣列模型、信號處理方法和估計的角度,提出一種互質(zhì)陣列模型,并與現(xiàn)有的算法做相關(guān)比較,以提高算法的性能。從應用角度出發(fā),為了增加陣列的孔徑,論文介紹了互質(zhì)陣列的概念,通過相應的數(shù)學模型分析對其原理做了簡要闡述。將互質(zhì)陣列與傳統(tǒng)的陣列模型進行比較,然后把互質(zhì)陣列與波達方向角估計進行結(jié)合。在壓縮感知基本原理的基礎(chǔ)上,對空間譜估計方法以及稀疏空間類算法進行進一步比較,并設計出基于稀疏類的DOA估計方法。在上述理論基礎(chǔ)上,論文又研究了子空間類的水聲互質(zhì)陣列DOA估計方法。將子空間類算法與水聲互質(zhì)陣列模型相結(jié)合,并進行基于水聲互質(zhì)陣列的DOA估計方法相關(guān)研究。此外,本文將稀疏重構(gòu)算法應用于水聲互質(zhì)陣列的DOA估計中,將互質(zhì)陣列條件下的稀疏重構(gòu)算法和傳統(tǒng)DOA估計算法的估計性能進行比較。此外還對比了稀疏重構(gòu)理論下均勻陣列與互質(zhì)陣列的DOA估計方法。經(jīng)過仿真實驗,本文所提的陣列結(jié)構(gòu)比均勻陣列更符合實際需要,所提的DOA方法也比以往的DOA估計方法具有更高的估計精度。兩種方法相結(jié)合則使得估計效果比傳統(tǒng)的DOA估計方法優(yōu)越得多,即在陣元數(shù)目相同的情況下,能夠擴展陣列孔徑,增加可識別的信源數(shù)目,節(jié)約經(jīng)濟成本。
[Abstract]:With the increasing application of sensor and array technology, array signal processing has become a hot spot in recent years. As a part of array signal processing, the theory of DOA of Arrival) estimation has matured gradually in recent decades, and has been widely used in many fields such as sonar, communication, radar, medicine and so on. Due to the limitations of environment, hardware, energy supply and so on, the existing DOA estimation methods of underwater acoustic array often appear the problems of slow operation and low precision in practical application. Most of the existing DOA estimation methods are based on uniform array. By enlarging the aperture of the array to improve the angular resolution of the spatial spectrum estimation method, the engineering cost will be increased sharply. Therefore, without increasing the cost of azimuth estimation, a new array model has economic significance. From the angle of array model, signal processing method and estimation of underwater acoustic array signal, this paper proposes a kind of mutual-mass array model, and compares it with the existing algorithms to improve the performance of the algorithm. From the point of view of application, in order to increase the aperture of array, the concept of mutual-mass array is introduced in this paper, and its principle is briefly explained by the corresponding mathematical model analysis. The mutual prime array is compared with the traditional array model, and then the DOA estimation is combined. Based on the basic principle of compressed sensing, the spatial spectrum estimation method and sparse spatial class algorithm are further compared, and a sparse class based DOA estimation method is designed. On the basis of the above theory, the subspace DOA estimation method of underwater acoustic mass array is studied. The subspace algorithm is combined with the underwater acoustic quality array model, and the DOA estimation method based on the underwater acoustic quality array is studied. In addition, the sparse reconstruction algorithm is applied to DOA estimation of underwater acoustic mutual-mass arrays, and the estimation performance of sparse reconstruction algorithm under the condition of mutual-mass array is compared with that of the traditional DOA estimation algorithm. In addition, the DOA estimation methods of uniform array and mutual-mass array under sparse reconstruction theory are compared. The simulation results show that the array structure proposed in this paper is more suitable to the practical needs than the uniform array, and the DOA method proposed in this paper has higher estimation accuracy than the previous DOA estimation methods. The combination of the two methods makes the estimation effect much better than the traditional DOA estimation method, that is, when the number of array elements is the same, the array aperture can be expanded, the number of identifiable sources can be increased, and the economic cost can be saved.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN911.7

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本文編號：2056322