水声互质阵列DOA估计方法研究

发布时间：2018-06-23 07:20

本文选题：压缩感知 + 稀疏重构　；参考：《江苏科技大学》2017年硕士论文

【摘要】：随着传感器和阵列技术应用的逐渐增多,阵列信号的处理成为近年的热点。波达方向(DOA,Direction of Arrival)估计作为阵列信号处理的一部分,经过近几十年的发展,其理论已日渐成熟,并广泛应用于声呐、通信、雷达、医学等众多领域。由于环境、硬件、能量供应等各方面的限制,现有的水声阵列DOA估计方法在实际应用中往往会出现运算慢,精度不高等问题。已有的DOA估计方法大多是基于均匀阵列的基础上进行研究的,通过扩大阵列的孔径达到提高空间谱估计方法测角分辨率的目的,会使工程成本的急剧增加。所以在不增加方位估计成本的前提下,提出一种新型阵列模型具有经济型的意义。本文从水声阵列信号的阵列模型、信号处理方法和估计的角度,提出一种互质阵列模型,并与现有的算法做相关比较,以提高算法的性能。从应用角度出发,为了增加阵列的孔径,论文介绍了互质阵列的概念,通过相应的数学模型分析对其原理做了简要阐述。将互质阵列与传统的阵列模型进行比较,然后把互质阵列与波达方向角估计进行结合。在压缩感知基本原理的基础上,对空间谱估计方法以及稀疏空间类算法进行进一步比较,并设计出基于稀疏类的DOA估计方法。在上述理论基础上,论文又研究了子空间类的水声互质阵列DOA估计方法。将子空间类算法与水声互质阵列模型相结合,并进行基于水声互质阵列的DOA估计方法相关研究。此外,本文将稀疏重构算法应用于水声互质阵列的DOA估计中,将互质阵列条件下的稀疏重构算法和传统DOA估计算法的估计性能进行比较。此外还对比了稀疏重构理论下均匀阵列与互质阵列的DOA估计方法。经过仿真实验,本文所提的阵列结构比均匀阵列更符合实际需要,所提的DOA方法也比以往的DOA估计方法具有更高的估计精度。两种方法相结合则使得估计效果比传统的DOA估计方法优越得多,即在阵元数目相同的情况下,能够扩展阵列孔径,增加可识别的信源数目,节约经济成本。
[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.
【学位授予单位】：江苏科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7

【参考文献】