密集目标的空间谱估计与稳健波束形成技术

发布时间：2018-11-04 17:03

【摘要】：空间谱估计与自适应波束形成技术作为阵列信号处理的主要应用方向,广泛被应用于雷达、声呐和通信等领域。但是,当来波信号中存在邻近的强弱目标时,强信号会掩盖弱信号的信息,准确估计弱信号波达方向困难。此外,阵列响应误差、阵元位置误差以及期望信号出现在样本数据中等因素使得自适应波束形成算法性能下降明显。本文针对上述问题进行了研究,主要工作包括:(1)以多重信号分类(multiple signal classification,Music)算法为参考,对比分析了典型的强弱信号信源波达角(Direction of Arrival,DOA)估计算法,包括RELAX算法、干扰阻塞法以及SSMusic算法。明确了它们的适用情况和优缺点。在此基础之上,通过交换大特征值顺序构造的伪数据协方差矩阵,提出了一种基于伪信号协方差矩阵的强弱邻近信源DOA估计方法。该方法减小了对硬件的要求,实现过程简单。最后,克服了经典方法中抑制强信号时对邻近弱信号引入损失的缺点,提出了基于最大似然的强弱邻近信源DOA估计方法。理论分析与仿真实验证明了所提方法的正确性与有效性。(2)针对期望信号出现在样本数据中,出现信号相消现象以及自适应波束形成算法对阵列误差敏感的问题,研究了经典的提升自适应波束形成算法稳健性的方法,如信号特征子空间算法、对角加载算法、最差情况下性能最优算法、二次规划算法以及干扰加噪声协方差矩阵重构算法等。详细描述了这些经典方法提升自适应波束形成算法的基本思想与实现途径,并明确了这些方法的性能与适用情况,最后通过仿真实验分析了方法的有效性与优缺点。
[Abstract]:Spatial spectrum estimation and adaptive beamforming are widely used in radar, sonar and communication as the main applications of array signal processing. However, when there are adjacent strong and weak targets in the incoming signal, the strong signal will cover up the information of the weak signal, and it is difficult to accurately estimate the direction of arrival of the weak signal. In addition, the performance of adaptive beamforming algorithm is obviously degraded by array response error, element position error and expected signal appearing in the sample data. The main work of this paper is as follows: (1) based on the multi-signal classification (multiple signal classification,Music) algorithm, the typical strong and weak signal source (Direction of Arrival,DOA) estimation algorithms are compared and analyzed. Including RELAX algorithm, jamming blocking method and SSMusic algorithm. Their application and advantages and disadvantages are clarified. On this basis, by exchanging the pseudo-data covariance matrix constructed in the order of large eigenvalues, a DOA estimation method based on pseudo-signal covariance matrix is proposed. The method reduces the requirement of hardware, and the process is simple. Finally, this paper overcomes the disadvantage of introducing loss to the adjacent weak signal when suppressing the strong signal in the classical method, and proposes a strong and weak DOA estimation method based on the maximum likelihood. Theoretical analysis and simulation results show that the proposed method is correct and effective. (2) aiming at the problem of the expected signal appearing in the sample data, the phenomenon of signal cancellation and the sensitivity of the adaptive beamforming algorithm to the array error, The classical lifting adaptive beamforming algorithm is studied, such as signal feature subspace algorithm, diagonal loading algorithm, and worst case performance optimization algorithm. Quadratic programming algorithm and interference and noise covariance matrix reconstruction algorithm. This paper describes in detail the basic ideas and implementation approaches of these classical lifting adaptive beamforming algorithms, and clarifies the performance and application of these methods. Finally, the effectiveness, advantages and disadvantages of these methods are analyzed through simulation experiments.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.7

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