基于局部搜索的鲁棒自适应波束形成算法研究

发布时间：2019-06-25 07:51

【摘要】：近年来,阵列信号处理作为信号和通信领域的一个重要分支,在国民经济及军事各个领域获得了广泛应用和迅速发展。而自适应波束形成算法作为自适应阵列天线技术的核心受到了学者的广泛关注。在实际的应用环境中往往存在许多误差和非理想因素,如波达方向估计误差、非均匀传输介质、信号波前失真、传感器阵列的校准误差和有限的采样数目带来的估计误差等等。这些因素导致的导向矢量失配会使自适应波束形成算法的性能严重下降,因此研究实际通信系统环境下的鲁棒自适应波束形成算法具有非常重要的意义。本文对常见的自适应波束形成算法进行了系统的研究,并在此研究基础上,提出了一种基于局部搜索的鲁棒自适应波束形成算法,该种算法在导向矢量误差较大时可以显著地提高波束形成器的性能。其中主要内容包含以下几点：1.介绍了阵列天线统计模型和波束形成的基本理论知识,对波束形成的原理及阵列方向图函数做了详细描述。概述了自适应波束形成算法的三种最常见的性能度量准则,并引出了经典自适应波束形成算法。2.对已有的鲁棒自适应波束形成算法进行了研究。首先由采样协方差矩阵求逆算法引出了对角加载采样协方差矩阵求逆算法,该算法具有较好的鲁棒性。接着对两种最常见的鲁棒波束形成算法——鲁棒Capon波束形成感算法和最差性能最优算法做了详细的分析和理论推导。接下来在鲁棒Capon来成算法的基础上,引入迭代思想,以小的不确定集的不断迭代来逼近实际信号导向矢量,对迭代鲁棒最小方差波束形成算法进行了分析。3.在前面一系列研究的基础上,针对在多种失真环境中出现的导向矢量失配误差较大的情况,本文提出一种基于局部搜索的鲁棒自适应波束形成算法。该算法首先通过一种优化方法对波达角度进行估计,再以估计出的导向矢量为参照使用最差性能最优算法,最终达到提高系统输出信干噪比的目的。最后,对该算法进行仿真验证,仿真结果表明,本算法明显提高了波束形成器的性能,且优于其它算法。
[Abstract]:In recent years, array signal processing, as an important branch of signal and communication field, has been widely used and developed rapidly in various fields of national economy and military. As the core of adaptive array antenna technology, adaptive beamforming algorithm has been widely concerned by scholars. There are many errors and non-ideal factors in practical application environment, such as direction of arrival estimation error, non-uniform transmission medium, signal wavefront distortion, calibration error of sensor array and estimation error caused by limited number of samples, and so on. The mismatch of guidance vector caused by these factors will seriously degrade the performance of adaptive beamforming algorithm, so it is very important to study the robust adaptive beamforming algorithm in practical communication system environment. In this paper, the common adaptive beamforming algorithms are systematically studied, and on the basis of this research, a robust adaptive beamforming algorithm based on local search is proposed, which can significantly improve the performance of beamformer when the guidance vector error is large. The main contents include the following points: 1. The statistical model of array antenna and the basic theoretical knowledge of beamforming are introduced, and the principle of beamforming and the function of array pattern are described in detail. The three most common performance metrics of adaptive beamforming algorithm are summarized, and the classical adaptive beamforming algorithm is introduced. 2. The existing robust adaptive beamforming algorithms are studied. Firstly, the inverse algorithm of diagonal loaded sampling covariance matrix is introduced from the inverse algorithm of sampling covariance matrix. The algorithm has good robustness. Then, two of the most common robust beamforming algorithms, robust Capon beamforming sensing algorithm and worst performance optimal algorithm, are analyzed and deduced in detail. Then, on the basis of robust Capon algorithm, the iterative idea is introduced, and the actual signal guidance vector is approximated by the continuous iteration of small uncertain sets, and the iterative robust minimum variance beamforming algorithm is analyzed. On the basis of the previous series of studies, a robust adaptive beamforming algorithm based on local search is proposed to solve the problem of large mismatch error of guidance vector in a variety of distortion environments. Firstly, the arrival angle is estimated by an optimization method, and then the worst performance optimization algorithm is used to improve the output signal-to-interference noise ratio (SNR) of the system by using the estimated guidance vector as the reference. Finally, the simulation results show that the algorithm improves the performance of beamformer and is superior to other algorithms.
【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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