【作者】 李书；

【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， 2017， 硕士

【摘要】 阵列信号处理是现代信号处理领域的一个重要分支,其利用传感器阵列来接收空间信号,与传统的单个定向传感器相比,具有灵活的波束控制、高的信号增益、极强的干扰抑制能力及高的空间分辨能力等优点。参数估计是阵列信号处理研究的一类很重要的问题,包括空间信号的波达方向(Direction of Arrival,DOA)估计、频率估计和极化参数估计。平行因子分析方法是阵列信号处理中常见的参数估计算法,借助压缩感知,往往可以降低空间存储需求和计算复杂度。本文主要研究基于压缩感知平行因子框架的阵列参数估计算法,选题具有理论意义和应用价值。本文工作如下:1)研究均匀面阵中一种基于三线性压缩感知的二维角度估计算法,该算法将压缩感知理论与平行因子分析方法结合起来,实现面阵中的二维角度估计。该算法无需谱峰搜索,能够实现参数自动配对。由于压缩,该算法的计算复杂度低于传统的三线性分解算法。仿真结果表示,该算法的角度估计性接近于传统的三线性分解算法,且优于借助旋转不变性进行信号参数估计(Estimating Signal Parameters via Rotational Invariance Techniques,ESPRIT)算法。2)提出线阵中一种基于三线性压缩感知的角度与频率联合估计算法,将压缩感知理论与平行因子框架结合起来,实现DOA和频率的联合估计。分析算法的计算复杂度,并和传统的平行因子方法进行对比,该算法拥有更低的计算复杂度。此外,我们推导了参数估计的克拉美罗界(Cramer-Rao bound,CRB)。仿真结果表明,该算法的角度和频率估计性能优于ESPRIT算法和传播算子(Propogator Method,PM),且接近于传统的三线性分解算法。该算法能够自动配对角度和频率估计的结果,且同时适用于均匀和非均匀线阵。3)提出一种电磁矢量阵中基于四线性压缩感知的发射角(Direction of Departure,DOD)和到达角(DOA)估计算法来解决电磁矢量多输入多输出(Multiple-Input-Multiple-Output,MIMO)雷达中的角度估计问题。首先将接收信号构建成四线性模型,利用压缩矩阵进行压缩,随之利用四线性交替最小二乘算法进行四线性分解,最后通过稀疏恢复问题的求解获得DOD/DOA的估计。借助压缩过程,该算法降低了传统基于四线性分解的角度估计算法的计算复杂度。文中给出了复杂度分析和克拉美罗界推导。该算法的角度估计性能优于ESPRIT算法,且接近传统的四线性分解算法。更多还原

【Abstract】 Array signal processing is an important branch in the field of modern signal processing,which utilizes the sensor array to receive the signal.Compared with the traditional single directional sensors,sensor arrays have the advantsges of more flexible beam control,higher signal gain,stronger interference suppression ability and higher spatial resolution.An important issue of array signals processing is the parameters estimation,including the estimation of direction of arrival(DOA),frequency and polarimetric parameters.Parallel factor analysis is a common method for parameter estimation in array signal processing.Usually,the storage space requirement and computational complexity of this method can be reduced by combinging with compressed sening.Multi-parameter estimation algorithms via compressed sensing parallel factor framework are investigated in this paper,which has scientific significance and practical values.The main work in this paper is summarized as follows.1)A compressed sensing trilinear decomposition-based algorithm is studied for the two-dimensional angle estimation algorithm of uniform rectangle array.The algorithm needs no spectrum peak searching,and achieves automatically paired two-dimensional angle estimation.Owing to compression,the algorithm has lower computational complexity.Simulation results verify that the algorithm has close angle estimation performance to the conventional trilinear model-based algorithm,and it outperforms the estimation of signal parameters via rotational invariance techniques(ESPRIT)algorithm.2)A compressed sensing trilinear decomposition-based algorithm is proposed for joint direction of arrival(DOA)and frequency estimation of narrow-band signals with linear array.Comparison of the computational complexity between the proposed algorithm and the conventional trilinear model-basded algorithm is presented in the paper,which verifys that the proposed alfgorithm has lower computational complexity.Besides,Cramer-Rao bounds(CRBs)of the angle and frequency estimation are derived.The DOA and frequency estimation performance of the proposed algorithm is very close to that of the conventional PARAFAC algorithm,and better than that of ESPRIT algorithm and the propagator method(PM).Furthermore,the proposed algorithm can achieve automatically paired DOA and frequency estimation.Besides,it is applicable for both uniform and non-uniform linear arrays.3)A compressed sensing quadrilinear decomposition-based algorithm is proposed for direction of departure(DOD)and DOA estimation for bistatic MIMO radar with electromagnetic vector sensors.In this algorithm,the received data is firstly arranged into a quadrilinear model and then it is compressed according to the compressed sensing theory.Then quadrilinear decomposition is conducted on the compressed quadrilinear data model via the quadrilinear alternating least square algorithm and finally obtain the automatically paired angle estimates with sparsity.Owing to compression,the proposed algorithm has smaller storage requirement and lower computational complexity than the conventional quadrilinear decomposition-based algorithm.The algorithm has higher angle estimation accuracy than ESPRIT algorithm and its estimation performance is close to that of the conventional quadrilinear decomposition-based algorithm.The CRB of angle estimation is also dereived.更多还原