【作者】 张胜男；

【导师】 张小飞；

【作者基本信息】 南京航空航天大学 ， 通信与信息系统， 2008， 硕士

【摘要】 随着移动通信的飞速发展和新兴业务的不断涌现,越来越多的研究开始关注于智能天线技术。智能天线技术以其独特的抗多址干扰和扩容能力,不仅是目前解决个人通信中多址干扰、容量限制等问题的最有效的手段,也被公认为是将来移动通信的一种发展趋势。阵列信号参数估计是智能天线技术中必不可少的技术之一。本文研究了三种阵列天线(均匀线阵、均匀圆阵、平面阵列)中的主要阵列参数估计。全文的主要工作如下:简单介绍了智能天线技术的特点,回顾了阵列信号处理的发展和阵列信号参数估计的经典方法。介绍了平行因子(PARAFAC)的相关背景与知识,研究了三维数据分析的基本概念及基本方法,主要介绍了平行因子模型的分解方法:三线性交替最小二乘(TALS:Trilinear Alternating Least Square)算法和TALS的快速算法――COMFAC(COMplex parallel FACtor analysis)算法,其中COMFAC算法是后续章节的仿真实验所采取的主要算法。将平行因子应用到联合角度和频率估计中,对多时延的阵列接收信号进行分析,分析表明此时接收信号可以表征为三线性模型,因此提出了均匀线阵下的基于三线性分解的联合角度和频率估计算法。仿真结果表明该算法具有较好的性能,在低快拍数的情况下仍然可以较好的工作。将平行因子技术应用到联合角度和时延估计中,提出了均匀线阵/均匀圆阵下基于三线性分解的盲联合角度和时延估计算法。传统联合角度和时延估计方法在过载情况下(用户数大于阵元数)不能正确估计。与传统联合角度和时延估计方法相比,基于三线性分解的算法不仅具有较好角度和时延估计性能,而且有着较快收敛速度,是一种盲的、鲁棒的处理方法,在低快拍下也有较好的性能。将平行因子技术应用到DOA估计中,对平面阵列的输出信号进行分析表明,此信号具有三线性模型特征,因此提出了平面阵列下基于三线性分解的二维方向角估计算法。仿真结果表明该算法具有较好的DOA估计性能,而且有着较快的收敛速度,是一种盲的、鲁棒的处理方法。更多还原

【Abstract】 With the fast development of mobile communications and emerging services, an increasing number of scientists start to focus on smart antenna technique. In early 90’s, smart antenna technique has been applied to commercial mobile communications; it is an important breakthrough in mobile communication techniques and is universally recognized as a general tendency in future. The parameter estimation of array signals is an essential part of smart antenna technique. The parameter estimation of three kinds of antenna arrays(uniform linear array, uniform circular array and square array) is investigated in this paper. The main contents are as follows:The characteristics of smart array technique and classic methods of parameter estimation are simply introduced.The background and knowledge of PARAFAC technique are introduced which mainly focus on analysis of 3-dimension data. Two important methods of decomposition of PARAFAC model are introduced: TALS(Trilinear Alternating Least Square) and COMFAC(COMplex parallel FACtor analysis). COMFAC method will be used in the following chapters as a faster algorithm in simulations.Joint angle and frequency estimation based on parallel factor technique is investigated. A joint angle and frequency estimation method based on trilinear alternating least square in uniform linear array is presented. This algorithm relies on a fundamental result regarding the uniqueness of low-rank three-way array decomposition and has better performance, and supports small sample sizes.Blind joint angle and delay estimation method based on parallel factor technique is investigated. A blind joint angle and delay estimation method based on complex parallel factor in uniform linear array and a blind joint angle and delay estimation method based on complex parallel factor in uniform circular array are presented in this paper to improve convergence performance. The simulation results reveal that the presented algorithms have better performance of the estimation of delay and DOA. They have better convergence , and work well in low snapshots. Furthermore, they are blind and robust.DOA estimation based on PARAFAC technique is investigated. A DOA estimation method based on COMFAC in square array is presented. Compared with traditional methods ,the presented one has better DOA estimation performance and faster convergence rate . It also has blind and robust characteristic.更多还原