基于非均匀阵列的波达方向估计技术研究
发布时间：2019-02-18 11:35
【摘要】：波达方向(DOA)估计是信号处理领域的研究热点之一,这一技术应用广泛,在通信、雷达等领域发挥着越来越重要的作用。在过去的几十年里人们不断提出了一系列高效的DOA估计算法。近几年来,随着压缩感知理论的不断成熟和广泛应用,基于稀疏重构技术的DOA估计算法受到更多的关注和研究。使用均匀线阵估计信号的波达方向时,阵列的自由度直接受限于阵元个数,采用空间平滑技术处理相关/相干信号则会进一步导致阵列孔径变小、分辨率降低以及自由度损失。由于非均匀阵列能够获得更大的阵列孔径和更高的自由度,并且阵元的摆放位置也具有更大的灵活性,所以研究非均匀阵列的设计和相应的DOA估计算法具有很好的实际意义和应用价值。本文以使用非均匀阵列估计远场窄带信号的波达方向为研究目标,主要研究如何设计非均匀阵列以提高阵列的自由度,并根据非均匀阵列的特点设计相应的DOA估计算法,对互质阵列、嵌套阵列以及部分均匀阵列等方法进行了研究和仿真分析。针对相关/相干信号的DOA估计,提出了两种基于稀疏重构技术的DOA估计方法。本论文的主要贡献和创新点是:1.针对使用非均匀阵列估计相关/相干信号的波达方向问题,提出了一种稀疏重构辅助的相关信号DOA估计方法。该方法主要利用稀疏重构技术对信号的波达方向进行初始估计并确定内插映射空域,相比于已有的估计方法,本方法具有更高的估计精度。2.为了达到既能提高阵列的自由度又能估计相关/相干信号的目的,提出了一种基于协方差的低维度迭代稀疏重构算法。与传统的子空间方法相比,该方法能够获得更大的阵列孔径和更高的自由度,而且该方法比已有的稀疏协方差算法的计算复杂度更小。
[Abstract]:Direction of arrival (DOA) estimation is one of the research hotspots in the field of signal processing. This technology is widely used and plays an increasingly important role in communication, radar and other fields. In the past few decades, a series of efficient DOA estimation algorithms have been proposed. In recent years, with the maturity and wide application of compressed sensing theory, DOA estimation algorithm based on sparse reconstruction technology has received more attention and research. When the DOA of the signal is estimated by uniform linear array, the degree of freedom of the array is directly limited by the number of the array elements. The spatial smoothing technique can further reduce the aperture of the array, reduce the resolution and lose the degree of freedom by using the spatial smoothing technique to process the correlation / coherent signal. Because the non-uniform array can obtain larger aperture and higher degree of freedom of the array, and the position of the array elements is also more flexible, Therefore, it has good practical significance and application value to study the design of non-uniform array and the corresponding DOA estimation algorithm. In this paper, the direction of arrival (DOA) of far-field narrow-band signals is estimated by using non-uniform arrays as the research object. This paper mainly studies how to design non-uniform arrays to improve the degree of freedom of the arrays, and designs the corresponding DOA estimation algorithm according to the characteristics of non-uniform arrays. The methods of mass array, nested array and partial uniform array are studied and simulated. For the DOA estimation of correlated / coherent signals, two DOA estimation methods based on sparse reconstruction technique are proposed. The main contributions and innovations of this paper are as follows: 1. To solve the problem of estimating the direction of arrival (DOA) of correlation / coherent signals using non-uniform arrays, a sparse reconstruction aided DOA estimation method for correlated signals is proposed. This method mainly uses sparse reconstruction technique to estimate the DOA of the signal and determine the interpolation mapping spatial domain. Compared with the existing estimation methods, the proposed method has a higher estimation accuracy. 2. In order to improve the degree of freedom of the array and estimate the correlation / coherent signals, a low dimensional iterative sparse reconstruction algorithm based on covariance is proposed. Compared with the traditional subspace method, this method can obtain larger array aperture and higher degree of freedom, and the computational complexity of this method is less than that of the existing sparse covariance algorithm.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7
本文编号：2425792
[Abstract]:Direction of arrival (DOA) estimation is one of the research hotspots in the field of signal processing. This technology is widely used and plays an increasingly important role in communication, radar and other fields. In the past few decades, a series of efficient DOA estimation algorithms have been proposed. In recent years, with the maturity and wide application of compressed sensing theory, DOA estimation algorithm based on sparse reconstruction technology has received more attention and research. When the DOA of the signal is estimated by uniform linear array, the degree of freedom of the array is directly limited by the number of the array elements. The spatial smoothing technique can further reduce the aperture of the array, reduce the resolution and lose the degree of freedom by using the spatial smoothing technique to process the correlation / coherent signal. Because the non-uniform array can obtain larger aperture and higher degree of freedom of the array, and the position of the array elements is also more flexible, Therefore, it has good practical significance and application value to study the design of non-uniform array and the corresponding DOA estimation algorithm. In this paper, the direction of arrival (DOA) of far-field narrow-band signals is estimated by using non-uniform arrays as the research object. This paper mainly studies how to design non-uniform arrays to improve the degree of freedom of the arrays, and designs the corresponding DOA estimation algorithm according to the characteristics of non-uniform arrays. The methods of mass array, nested array and partial uniform array are studied and simulated. For the DOA estimation of correlated / coherent signals, two DOA estimation methods based on sparse reconstruction technique are proposed. The main contributions and innovations of this paper are as follows: 1. To solve the problem of estimating the direction of arrival (DOA) of correlation / coherent signals using non-uniform arrays, a sparse reconstruction aided DOA estimation method for correlated signals is proposed. This method mainly uses sparse reconstruction technique to estimate the DOA of the signal and determine the interpolation mapping spatial domain. Compared with the existing estimation methods, the proposed method has a higher estimation accuracy. 2. In order to improve the degree of freedom of the array and estimate the correlation / coherent signals, a low dimensional iterative sparse reconstruction algorithm based on covariance is proposed. Compared with the traditional subspace method, this method can obtain larger array aperture and higher degree of freedom, and the computational complexity of this method is less than that of the existing sparse covariance algorithm.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7
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相关期刊论文 前2条
1 林波;张增辉;朱炬波;;基于压缩感知的DOA估计稀疏化模型与性能分析[J];电子与信息学报;2014年03期
2 熊波;李国林;尚雅玲;高云剑;;信号相关性与DOA估计[J];电子科技大学学报;2007年05期
，本文编号：2425792
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