基于稀疏分解的阵列幅相误差自校正

针对现实条件下普遍存在的阵元通道幅相误差对波达方向DOA(Directions of Arrival)估计的影响,提出一种新的阵列误差自校正方法.该方法对接收信号进行稀疏分解,结合遗传算法寻优,可以精确估得信号源的波达方向,同时估计出各阵元的幅相误差值.该方法适用于任意阵列形式,在小于0 dB信噪比的情况下也能保持较好的估计准确性.计算机仿真验证了方法的有效性.     

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。     

L型阵列通道不一致及阵元位置误差的联合校正方法

针对L型阵列中通道不一致和阵元位置误差同时存在的情况,利用辅助阵列和校正源,提出一种L型阵列通道不一致和阵元位置误差的解耦合估计方法.该方法计算量小,不需要误差参数的任何先验知识,且校正源的位置可以未知.理论分析和仿真结果表明,提出的方法能很好地解决L型阵列中通道不一致和阵元位置误差的联合校正问题,且两种误差的估计精度高.     

改进的阵列幅相差和阵元位置误差自校正算法

基于信号子空间拟合算法的代价函数,提出了一种改进的阵列幅相差和阵元位置误差自校正算法,该算法采用分步迭代的方式,可在阵列存在幅相差和阵元位置误差的情况下,估计出信源的波达方向(DOA)、阵列幅相差和阵元位置误差.文中还给出了参数估计惟一性的必要条件.计算机仿真结果验证了该算法的有效性.     

基于均匀十字阵的幅相误差自校正算法

针对实际应用中普遍存在的幅相误差扰动问题,结合子空间类波达方向估计算法,基于均匀十字阵列,提出了一种幅相误差的自校正算法。均匀十字阵列可分解为两个相互垂直的均匀线性阵列,利用均匀线阵接收数据协方差矩阵的结构特点对幅相误差进行初步估计,计算出波达方向的初始估计值,通过迭代方法得到更精确的估计值,自校正方法不需要任何参数初始值,实现比较简单。仿真实验验证了算法具有良好的误差校正效果,能够比较准确地估计出信源的波达方向角和阵元的幅相误差值。     

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.     

基于多对称虚拟变换的二维波达方向估计算法

提出一种多对称虚拟变换二维波达方向估计算法,通过对阵列进行多对称虚拟变换,得到多个分别关于原阵列对称的虚拟阵列,进而构造虚实平移不变子阵。利用该平移不变虚拟子阵的旋转不变因子,获得信号源的俯仰角,经多信号分类(MUSIC)一维搜索获得信号源方位角,方位角和俯仰角可自动配对。由于使用了各虚拟变换阵列的累加数据,累加运算使得各虚拟阵列数据的正负误差相互抵消,显著降低了虚拟变换矩阵导致的虚拟阵列和真实阵列之间的数据误差,提高了二维波达方向的估计精度。仿真实验研究了所提算法在信噪比、快拍数变化情况下,二维波达方向估计的均方根误差和成功率性能,证明了所提算法的有效性。     

基于内插阵列变换的扩展传播算子实值算法

针对内插阵列变换(VIA)思想在非圆信号波达方向(DOA)估计算法中的应用问题,提出一种基于内插阵列变换的扩展传播算子实值算法——VIA-EPM实值算法。利用真实阵列流型与虚拟阵列流型之间的变换矩阵,将真实阵列输出转换为虚拟阵列输出,再根据信号源为实数的特点,分别求取虚拟阵列输出的实部和虚部,将其串联组合,扩展阵列输出的维数,通过对扩展阵列输出矩阵进行分块并得出扩展传播算子,进而得到一种传播算子(PM)类的DOA估计算法。仿真实验表明：存在阵元位置误差的情况下,VIA-EPM实值算法通过对阵元位置校准数据进行内插阵列变换,取得与阵元位置校准的扩展传播算子实值算法(EPM实值算法)相当的估计性能,保持了阵列扩展能力、高估计精度以及高分辨力;并且在二维阵元位置误差情况下,其估计性能明显优于阵元位置未校准的EPM实值算法。结合VIA-EPM实值算法的计算复杂度分析可以看出：它同时获得了内插阵列变换技术以及信号非圆特性的优势;与复运算相比,其复杂度也相对降低。     

基于均匀圆阵的互耦误差校正算法研究

阵元之间的互耦效应严重影响了DOA的估计性能。基于均匀圆阵,提出了一种互耦条件下的波达方向估计和互耦误差自校正算法。利用带状循环矩阵的特性对均匀圆阵的互耦误差建立数学模型,再利用MUSIC算法和迭代法对互耦误差矩阵和波达方向同时进行估计,自校正方法无需任何辅助阵元即可实现两类参数的估计。仿真实验表明,算法很好地解决了均匀圆阵的互耦问题,能够比较准确地估计出波达方向角和互耦误差值。     

矢量天线阵元方向不一致的误差校正

基于特征值分解的子空间类算法对误差非常敏感,有必要对方向误差进行有效的校正.分析了方向误差形成的原因,建立了误差阵元的数据模型,研究同一入射信号玻印廷(Poynting)矢量与阵元排列方向的关系,使用3个参量未知的校正源,通过比较校正源玻印廷矢量在参考阵元与误差阵元处的响应,获得阵列采样数据方向误差校正矩阵.比较玻印延矢量的误差校正方法不涉及参量搜索,计算量小且易于在工程上实现,计算机仿真验证了算法的正确性和有效性.     

Array errors active calibration algorithm based on instrumental sensors

The array mutual coupling,gain-phase errors and sensor position errors would significantly degrade the performance of high-resolution direction of arrival (DOA) estimation algorithms.Aiming at the combined influences of the above three array errors,a kind of active calibration algorithm is presented with the help of instrumental sensors in this paper.Firstly,the integrated effects of the three array errors are shown to be equivalent to angularly dependent gain-phase errors.Then,a non-linear least square (LS...     

基于辅助阵元的阵列误差有源校正算法

阵列互耦、幅相误差和阵元位置误差的综合影响会严重影响超分辨率算法的性能,为此,针对上述3种阵列误差的综合影响,给出了一类基于辅助阵元的有源校正算法.首先,将3种阵列误差的综合影响等效为一种依赖方位变化的幅相误差,并通过一种基于辅助阵元的自校正方法得到了关于3种阵列误差的非线性最小二乘优化模型;然后,针对阵列误差矩阵可能出现的不同模型,给出了相应的求解算法;接着,针对算法的参数估计唯一性给出了较为细致的分析,并对算法做了进一步改进;最后,通过仿真实验验证了新算法的有效性.     

Array errors active calibration algorithm and its improvement

The combined effects of mutual coupling, gain and phase errors, and sensor position errors have great negative impact on the direction-finding performance of the MUSIC algorithm. This paper investigates the calibration of the array errors induced by the three errors. Based on the array errors calibration algorithm (algorithm 1) presented by See, two improved array error-calibrating algorithms are presented. One (algorithm 2) uses the sparseness of mutual coupling matrix, and the other (algorithm 3) utilizes...     

Array calibration of angularly dependent gain and phase uncertainties with carry-on instrumental sensors

Array calibration with angularly dependent gain and phase uncertainties has long been a difficult problem. Although many array calibration methods have been reported extensively in the literature, they almost all assumed an angularly independent model for array uncertainties. Few calibration methods have been developed for the angularly dependent array uncertainties. A novel and efficient auto-calibration method for angularly dependent gain and phase uncertainties is proposed in this paper, which is called ISM (Instrumental Sensors Method). With the help of a few well-calibrated instrumental sensors, the ISM is able to achieve favorable and unambiguous direction-of-arrivals (DOAs) estimate and the corresponding angularly dependent gain and phase estimate simultaneously, even in the case of multiple non-disjoint sources. Since the mutual coupling and sensor position errors can all be described as angularly dependent gain/phase uncertainties, the ISM proposed still works in the presence of a combination of     

Array geometry impact on music in the presence of spatially distributed sources

The MUltiple SIgnal Classification (MUSIC) estimator has been widely studied for a long time for its high resolution capability in the domain of the direction of arrival (DOA) estimation, with the sources assumed to be point. However, when the actual sources are spatially distributed with angular dispersion, the performance of the conventional MUSIC is degraded. In this paper, the impact of the array geometry on the DOA estimation of spatially distributed sources impinging on a sensor array is considered. Taking into account a coherently distributed source model, we establish closed-form expressions of the MUSIC-based DOA estimation error as a function of the positions of the array sensors in the presence of model errors due to the angular dispersion of the signal sources. The impact of the array geometry is studied and particular array designs are proposed to make DOA estimation more robust to source dispersion. The analytical results are validated by numerical simulations.¹     

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.     

基于子空间类法的阵列幅相误差校正方法

该文针对阵列误差条件下多目标方位高分辨估计问题,提出了一种基于子空间类法的阵列幅相误差校正方法,实现过程简单,效果显著。计算机仿真结果表明,该校正方法可以有效地改善子空间类方法的稳健性,提高其多目标分辨能力,而且方位参数估计精度良好,具有较好的工程应用前景。