一种阵列天线通道不一致性的校正算法

基于对3G通信网络中阵列天线通道不一致性校正算法的性能分析,提出了一种基于参考信号源的自适应阵列天线通道不一致性校正算法。该算法充分利用了B.Friedlander算法和注入参考信号的校正方法的优点,通过使用参考信号的到达角估计值信息,来自适应地校正阵列天线中各阵元参数的不一致性,这些参数包括增益、幅度和相位因子。计算机仿真实验表明,推荐的算法大大改善了MUSIC算法估计来波信号方向的准确性,其性能远远超过了已有的各种校正算法。     

矢量阵一种简单的相位误差校正方法

分析了矢量水听器阵的阵列流型和误差模型,针对矢量水听器阵列声压和振速通道存在相位误差问题,应用常规波束形成方法和MUSIC方法进行方位估计误差大。并对通道存在估计性能下降的问题,提出了一种简单的相位误差校正方法。利用阵列流型向量在信号子空间的投影,得出存在相位误差的阵列流型向量就是信号子空间的特征值为＂一＂的特征向量,通过与精确的阵列流型向量比较求出矢量阵的相位误差。最后,修正矢量阵的相位误差,得到准确的方位估计能力。通过计算机仿真,验证了算法的可行性和准确性。     

基于频域均衡的宽带阵列通道校正

通道失配会严重影响阵列天线的性能,需要校正失配.窄带阵列只需在中心频率上校正即可,而宽带阵列因带宽相对较大需要对整个通道特性采取均衡的方法来保证各通道间的幅度和相位一致性.首先建立了失配通道模型,然后介绍了基于最小二乘准则的频域均衡算法,最后通过计算机仿真验证了频域均衡算法的有效性.同时,新算法采用LFM信号作为校正信号,克服了时域校正时校正权值不收敛的问题.     

一种相控阵天线REV幅相校正方法的仿真与实践

介绍了基于傅里叶分析的旋转矢量算法(REV),并将该算法应用于相控阵天线各单元的初始幅相误差校正,给出了它的仿真分析和工程实现方法。所使用的校正方法区别于以往采用的REV校正方法,利于减少校正天线对相控阵收发信号的影响。仿真结果显示,校正后各通道的相位和幅度与人为设置的相位、幅度只差一个常数,表明方法是有效的;通过实际测试,校正后的天线方向图与近场校正结果逼近,EIRP比校正前提高1.4dB以上,验证了校正方法的功能和性能。     

二维矢量传感器阵波达方向估计方法研究

针对应用中矢量传感器阵列高精度的目标方位估计问题,为对空域信号检测,提出了一种利用单个已知信源的数据协方差矩阵进行阵列误差校正的方法,修正了三种空间谱估计算法,并将应用于实测数据分析.数值实验结果表明,矢量传感器阵列的分辨能力有显著提高,波达方向的估计精度也更加精确.通过比较,MUSIC算法与Capon波束形成对阵列误差十分敏感.校正后,Capon波束形成的目标定位性能优于常规波束形成和MUSIC算法,更加实用有效,对实际应用中目标方位估计方法地选取有一定的指导意义.     

基于Matlab的Capon波束形成和空域谱算法仿真分析

采用Matlab语言分别仿真了Capon波束形成,Bartlett和Music空域谱估计算法,仿真结果展示了阵列天线参数与天线波束图之间的关系,使复杂的天线参数直观化,算法的理论结果可视化.通过改变信号和噪声的方向,幅度以及调整阵列天线的参数,对比前后的天线波束图仿真结果的变化.通过分析仿真的结果,指出了这几种算法上的优缺点,为将来算法的改进和适用范围的扩大以及新的算法的提出打下了铺垫.     

GNSS干扰测向中的自适应阵元标校算法研究

在卫星导航系统优化设计的研究中,由于存在空间干扰,信息源方向测试误差较大。针对阵列通道之间存在幅相误差,导致子空间类高分辨测向算法测向性能下降的问题,提出一种快速自校正算法应用到GNSS接收机阵列通道幅相误差校正中。算法无需知道干扰方向,在准确估计出干扰个数的基础之上,采用分步迭代的方式,估计出存在幅相误差情况下的干扰波达方向,能够实现对多个干扰的同时校正。仿真结果表明,改进算法能够有效提高MUSIC算法的稳健性,提高对多干扰的分辨能力和估计精度,在GNSS接收机阵元标校中有良好的应用前景。     

相位激励赋形多波束形成算法分析及仿真实现

数字波束形成是天线技术与数字信号处理技术结合的产物,是智能天线实现空分多址的关键技术.采用相位激励赋形多波束形成技术,给出了智能天线数字多波束形成的优化算法,利用该算法对均匀圆环阵天线各单元权值进行计算,仿真实现了均匀圆环阵天线多波束方向图,在不同方向上同时得到了多个波束.该算法计算复杂度低,空间隔离度好.阵元呈圆环状分布,具有全向搜索特性,克服了直线阵搜索的缺点.该方法还可用于任意单元构成的不均匀阵列天线,以及波束展宽和扇形波束的形成.     

AgileDARN雷达内定标的方法与实现

AgileDARN雷达系统是一种基于全数字相控阵技术的灵敏型地基相干高频雷达系统，实现对中高纬度电离层的探测。雷达系统的幅度和相位不一致将会导致雷达发射波束和接收波束的波束指向偏移，副瓣电平抬高和天线增益下降，最终影响雷达的探测精度。针对AgileDARN雷达系统幅度和相位不一致性展开研究，提出了一种基于FPGA和C++软硬件相结合的内定标方法。在发射探测信号之前，分别发送接收定标信号和发射定标信号，通过雷达系统的各个接收和发射通道后，计算各个通道之间的幅度和相位差异，用补偿因子来实现对雷达多通道幅度和相位不一致性的校正。实测数据表明:校正后各通道间幅度误差小于0.2 dB，相位误差小于1°。在满足系统一致性要求的前提下，既保证了实时性，又节省了雷达系统成本。     

AgileDARN雷达内定标的方法与实现

AgileDARN雷达系统是一种基于全数字相控阵技术的灵敏型地基相干高频雷达系统,实现对中高纬度电离层的探测。雷达系统的幅度和相位不一致将会导致雷达发射波束和接收波束的波束指向偏移,副瓣电平抬高和天线增益下降,最终影响雷达的探测精度。针对AgileDARN雷达系统幅度和相位不一致性展开研究,提出了一种基于FPGA和C++软硬件相结合的内定标方法。在发射探测信号之前,分别发送接收定标信号和发射定标信号,通过雷达系统的各个接收和发射通道后,计算各个通道之间的幅度和相位差异,用补偿因子来实现对雷达多通道幅度和相位不一致性的校正。实测数据表明:校正后各通道间幅度误差小于0.2 dB,相位误差小于1°。在满足系统一致性要求的前提下,既保证了实时性,又节省了雷达系统成本。     

Joint direction of arrival estimation and array calibration for coprime MIMO radar

Compared to large-scale MIMO radar, coprime MIMO radar can achieve approximate estimation performance with reduced antenna number. In this paper, joint direction of arrival (DOA) estimation and array calibration for coprime multiple-input multiple-output (MIMO) radar is considered, and an iterative method for the estimations of DOA and array gain-phase errors is proposed. Based on the received data structure of coprime MIMO radar, trilinear decomposition is firstly adopted to obtain the estimations of transmit and receive direction matrices, which are perturbated by the gain-phase errors. Through equation transformation, the un-perturbated direction matrices and gain-phase errors can be iteratively updated based on Least squares (LS). Finally, the unique DOA estimation is determined from the intersection of transmit and receive direction matrices. The proposed algorithm achieves better DOA estimation and array calibration performance than other methods including estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm, multiple signal classification (MUSIC)-like algorithm and joint angle and array gain-phase error estimation (JAAGE) method, and it performs close to the method with ideal arrays. Multiple simulation results verify the algorithmic effectiveness of the proposed method.     

Matrix pencil method for simultaneously estimating azimuth and elevation angles of arrival along with the frequency of the incoming signals

In this paper we describe a method for simultaneously estimating the direction of arrival (DOA) of the signal along with its unknown frequency. In a typical DOA estimation problem it is often assumed that all the signals are arriving at the antenna array at the same frequency which is assumed to be known. The antenna elements in the array are then placed half wavelength apart at the frequency of operation. However, in practice seldom all the signals arrive at the antenna array at a single pre-specified frequency, but at different frequencies. The question then is what to do when there are signals at multiple frequencies, which are unknown. This paper presents an extension of the matrix pencil method to simultaneously estimate the DOA along with the operating frequency of each of the signals. This novel approach involves approximating the voltages that are induced in a three-dimensional antenna array, by a sum of complex exponentials by jointly estimating the direction of arrival (both azimuth and elevation angles) along with the carrier frequencies of multiple far-field sources impinging on the array by using the three-dimensional matrix pencil method. The matrix pencil method is a direct data domain method for approximating a function by a sum of complex exponentials in the presence of noise. The variances of the estimates computed by the matrix pencil method are quite close to the Cramer–Rao bound. Finally, we illustrate how to carry out the broadband DOA estimation procedure using realistic antenna elements located in a conformal array. Some numerical examples are presented to illustrate the applicability of this methodology in the presence of noise. It is shown that the variance decreases as the SNR increases. The Cramer–Rao bound for the estimators are also provided to illustrate the accuracy and the computational efficiency of this new methodology.     

A novel high degree of freedom sparse array with displaced multistage cascade subarrays

The coprime array is a recently developed sparse array that is widely used in direction-of-arrival (DOA) estimation. The degree of freedom (DOF) for existing coprime arrays relies on the virtual array model, which is limited to the array structure. Furthermore, the continuous virtual array aperture is restricted. This paper aims to propose a novel high DOF sparse array with displaced multistage cascade subarrays to further increase the length of the continuous virtual array and improve the DOF. Through cascading and separating the same sparse arrays, the continuous virtual array aperture is improved and the accuracy of the direction-of-arrival estimation is increased. It is proved that the range of the displaced distance for the maximized continuous virtual array can be obtained. The comparison between the array configuration presented in this paper and the existing common arrays are analyzed. The simulation experiments show that, compared with the uniform array, the coprime array, the nested array, and the coprime array with displaced subarrays (CADiS), the sparse array configuration proposed in this paper can significantly increase the aperture of the continuous virtual array, greatly improve the array DOF and the DOA estimation accuracy, and effectively estimate the DOA of multiple sources in underdetermined conditions.     

基于声矢量阵小样本数据的DOA估计研究

根据水下目标在其到达方位(DOA)搜索空间的稀疏性,采用稀疏分解理论实现了小样本、低信噪比条件下的声矢量阵DOA估计。通过分析,构造出基于声矢量阵阵列流型形式的过完备原子库,并采用正交匹配追踪算法得到目标的DOA估计。通过仿真,基于稀疏分解的声矢量阵DOA估计算法对单快拍数据进行处理,即可得到比较准确的DOA估计结果。对湖试数据进行了处理,验证了算法的有效性和优越性。     

Direction of arrival estimation for smart antenna in multipath environment using convolutional neural network

This article aims to present a novel direction of arrival (DOA) estimation strategy for smart antenna in multipath environment. The smart antenna is composed of 2 main parts: the DOA estimator and the switched‐beam system. In the first part, a DOA estimation method based on convolutional neural network (CNN) has been implemented. The CNN is capable to select the desired radiation beams of the switched‐beam antenna without knowing the number of source signals coming from different directions, and in the case of noncoherent and coherent signals. Simulation results have been presented to show the effectiveness of the proposed intelligent approach.     

Near optimal conformal antenna array structure for direction‐of‐arrival estimation

This article determines the near optimal conformal antenna array structure for direction‐of‐arrival (DOA) estimation through a comprehensive study on the planar and usual conformal antenna arrays including the cylindrical and hemispherical by using the directive antenna elements in all designs. To model the hemispherical structure, an improved multi‐face antenna array with three different tilts is proposed and compared with previous works in order to investigate the tilt effect and obtain the conclusive results. The Cramer‐Rao lower bound, multiple signal classification, and root‐mean‐square error algorithms are utilized to evaluate the estimation accuracy of all conformal structures. Finally, by comparing the estimation precision of all conformal structures it is shown that the purposed multi‐face structure as the hemispherical model has a better performance than other conformal structures in terms of the maximum angular coverage of the spatial resource. Moreover, the proposed study method in this article fully examines the impacts of the different conformal antennas geometric structure on the DOA estimation performance by involving the directive antennas radiation patterns.     

基于COLD阵列的联合稀疏重构信号DOA估计方法

针对窄带和宽带两种情形,提出了一种基于同点正交磁环偶极子矢量天线（Co-centered orthogonal loop and dipole,COLD）阵列的联合稀疏重构信号波达方向（Direction-of-arrival,DOA）估计方法。该方法首先构造极化-空间域协方差矩阵,并对其第一列进行稀疏表示,在此基础上利用COLD阵列可视为相互垂直的磁环阵列和偶极子阵列这一特点,采用l2-范数约束下的凸优化（l1-范数）联合稀疏重构技术实现信号DOA估计。仿真实验表明,该方法较之现有方法具有分辨力高、估计精度高等优点。     

遗传算法用于波达方向估计的线阵优化

利用遗传算法优化阵列设计来改善声目标波达方向估计性能.研究波达方向估计采用信号相位匹配原理的奇异值分解法SVDSPM,利用遗传算法对线阵进行了优化.优化时,阵列孔径不变,将阵元数目作为优化变量,优化目标是降低DOA估计误差.仿真结果标明:优化后在阵元数目减少的情况下,DOA估计偏差和均方误差更小.而且优化的阵列有好的低信噪比及宽带信号波达方向估计的能力.     

基于FFT的子孔径MUSIC波达方向估计

针对现 有的很多波达方向估计算法涉及到数据协方差矩阵的估计及其特征分解,甚至是求逆,导致 运算复杂度高的问题,提出了基于快速傅里叶变换的子孔径MUSIC波达方向估计算法 。首先将等距线阵的接收数据矢量均匀划分为4个子矢量,然后对各个子矢量分别求FFT。将 FFT的结果相干积累,并找到最大峰值点。最后,利用子矢量FFT的结果中与最大峰值点对应 的数据构造新的降维矢量,借助MUSIC算法进行波达方向估计。该方法避免了直接接收数据 的协方差矩阵估计和特征分解,有效地降低了运算量和计算复杂度,在阵元数和快拍数都较 多的情况下优越性尤为明显。计算机仿真验证了所提方法的有效性和优越性。     

电磁矢量互质阵中基于降维Capon的DOA和极化估计算法

将传统电磁矢量均匀阵列推广为电磁矢量互质阵列,突破了阵元间距不大于半波长的限制。提出了电磁矢量互质阵列中基于降维Capon的波达方向（Direction of arrival,DOA）和极化联合估计算法。该算法无需假设已知极化信息,且只需一维搜索,避免了多维搜索,可实现DOA和极化参数自动配对;与相同阵元数的均匀阵列相比,明显提高了角度估计性能,并拓展了天线孔径,具有相对较高的自由度,且降低了运算复杂度。相同阵列及参数条件下,本文算法的角度估计性能优于ESPRIT算法和三线性分解算法。