自适应数字波束形成以及稳健技术研究
发布时间：2018-09-10 11:10
【摘要】：波束形成是阵列信号处理中的一项关键技术,在雷达、声纳、通信、医疗中有广泛的应用。本文主要介绍了窄带自适应波束形成的一些主要算法以及自适应阵列旁瓣相消和稳健波束形成算法。在均匀线阵和窄带信号模型条件下,给出了自适应波束形成的几种算法,包括最大信噪比准则、最小均方误差准则、最小方差无畸变准则,以及最小均方算法和采样矩阵求逆算法。并且分析了各算法的原理和优缺点,以及影响阵列自适应波束形成性能的两个个主要因素,包括这列幅相误差和自相关矩阵的估计误差。理论上分析了自适应旁瓣相消系统的结构和算法原理,基于最小均方误差准则和采样矩阵求逆算法,给出了自适应权矢量的求解方法,针对传统旁瓣相消算法的缺陷又给出了另一种基于特征空间的算法,它能够在小样本情况下使旁瓣保持在一个较低水平。分析了造成波形不稳健的主要原因,并介绍了几种稳健的波束形成算法,LCMV算法有较大损失,因为它展宽了主瓣,引入了更多的噪声成份。对角加载法在SNR较大时性能有所下降,这是因为其固定的对角加载量无法随着SNR的增加而变化,而且也无法确定一个合适的加载量。特征空间法在信噪比较低时性能损较大,这是因为在低SNR时,容易出现子空间缠绕,使得特征空间法失效。Worst-Case有比较好的性能,但是其不确定集参数的选择仍然是一个问题。最后给出了一种基于二阶锥规划(Second-Order Cone Programming,SOCP)的迭代流程来近似原本的非凸优化问题,其避免了使用等式约束,从而节省了系统自由度,进一步提升了系统性能。在理论分析的同时分别给出了各算法的仿真和性能分析。
[Abstract]:Beamforming is a key technology in array signal processing, which is widely used in radar, sonar, communication and medical treatment. This paper mainly introduces some main algorithms of narrowband adaptive beamforming and adaptive array sidelobe cancellation and robust beamforming algorithm. Under the condition of uniform linear array and narrowband signal model, several adaptive beamforming algorithms are presented, including the maximum signal-to-noise ratio criterion, the minimum mean square error criterion, and the minimum variance distortion-free criterion. And the minimum mean square algorithm and sampling matrix inverse algorithm. The principle, advantages and disadvantages of the algorithms and the two main factors affecting the performance of the array adaptive beamforming are analyzed, including the amplitude and phase errors and the estimation errors of the autocorrelation matrix. The structure and algorithm principle of adaptive sidelobe cancellation system are analyzed theoretically. Based on the minimum mean square error criterion and the inverse algorithm of sampling matrix, the method of solving adaptive weight vector is given. Aiming at the defects of the traditional sidelobe cancellation algorithm, another feature space-based algorithm is proposed, which can keep the sidelobe at a lower level in the case of small samples. This paper analyzes the main causes of waveform unsoundness, and introduces several robust beamforming algorithms, such as LCMV algorithm, which has a great loss, because it broadens the main lobe and introduces more noise components. The performance of diagonal loading method decreases when SNR is larger, because its fixed diagonal loading cannot change with the increase of SNR, and a suitable loading amount can not be determined. When the SNR is low, the performance of the feature space method is large, because it is easy to appear subspace winding in low SNR, which makes the feature space method invalid. Worst-Case has better performance, but the parameter selection of its uncertain set is still a problem. Finally, an iterative process based on second-order cone programming (Second-Order Cone Programming,SOCP) is proposed to approximate the original non-convex optimization problem, which avoids the use of equality constraints and thus saves the degree of freedom of the system and further improves the performance of the system. At the same time, the simulation and performance analysis of each algorithm are given.
【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
[Abstract]:Beamforming is a key technology in array signal processing, which is widely used in radar, sonar, communication and medical treatment. This paper mainly introduces some main algorithms of narrowband adaptive beamforming and adaptive array sidelobe cancellation and robust beamforming algorithm. Under the condition of uniform linear array and narrowband signal model, several adaptive beamforming algorithms are presented, including the maximum signal-to-noise ratio criterion, the minimum mean square error criterion, and the minimum variance distortion-free criterion. And the minimum mean square algorithm and sampling matrix inverse algorithm. The principle, advantages and disadvantages of the algorithms and the two main factors affecting the performance of the array adaptive beamforming are analyzed, including the amplitude and phase errors and the estimation errors of the autocorrelation matrix. The structure and algorithm principle of adaptive sidelobe cancellation system are analyzed theoretically. Based on the minimum mean square error criterion and the inverse algorithm of sampling matrix, the method of solving adaptive weight vector is given. Aiming at the defects of the traditional sidelobe cancellation algorithm, another feature space-based algorithm is proposed, which can keep the sidelobe at a lower level in the case of small samples. This paper analyzes the main causes of waveform unsoundness, and introduces several robust beamforming algorithms, such as LCMV algorithm, which has a great loss, because it broadens the main lobe and introduces more noise components. The performance of diagonal loading method decreases when SNR is larger, because its fixed diagonal loading cannot change with the increase of SNR, and a suitable loading amount can not be determined. When the SNR is low, the performance of the feature space method is large, because it is easy to appear subspace winding in low SNR, which makes the feature space method invalid. Worst-Case has better performance, but the parameter selection of its uncertain set is still a problem. Finally, an iterative process based on second-order cone programming (Second-Order Cone Programming,SOCP) is proposed to approximate the original non-convex optimization problem, which avoids the use of equality constraints and thus saves the degree of freedom of the system and further improves the performance of the system. At the same time, the simulation and performance analysis of each algorithm are given.
【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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