共查询到19条相似文献,搜索用时 218 毫秒
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提出了一种近场方位和距离联合估计的无源定位算法.根据阵列信号协方差矩阵的Toeplitz特性,重构出只与信源方位角相关的近似远场协方差矩阵.对该协方差矩阵做子空间分解,通过方位估计的求根MUSIC算法得到对信源的方位角估计值;对信源距离的估计,定义了一种新的空间谱函数,仅通过一次一维搜索便可以得到所有距离谱峰;再将方位和距离配对进行简单的配对操作即完成信源的定位.最后通过计算机仿真验证了该算法的有效性. 相似文献
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基于高阶累积量的近场通信波达方向估计算法 总被引:1,自引:0,他引:1
通过对近场通信波达方向准确估计,提高目标信源的定位能力.传统方法中对近场源通信信源的波达方向估计采用多普勒估计方法,由于近场通信的空间信源为窄带信号,多普勒估计会导致DOA估计频谱失真.提出一种基于高阶累积量的近场通信波达方向估计算法.采用均匀间隔线列阵构建近场通信的信号模型,进行近场源目标特征构建,提取近场源通信信号的斜度和峰度等特征,采用高阶累积量特征提取方法,分别求得对应近场通信信源的方位角、频率和距离三维参数,使得每个信源的参数自动配对,提高了近场通信DOA波达方向估计的效率和精度,实现近场源通信信号的波达方向估计算法改进.仿真实验结果表明,采用该方法进行近场方法波达方向估计的精度较高,对信源方位的定位准确,性能优越于传统方法,在近场通信中具有较好的应用价值. 相似文献
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利用加权平滑l0范数(Smoothed l0, SL0)算法估计MIMO雷达目标DOA时,需要把协方差矩阵进行矢量化来获得相应的稀疏重构模型,并利用信号和噪声子空间的正交性来构造加权向量。然而当存在相干信源时,MIMO雷达协方差矩阵的秩将退化,这会使得稀疏重构模型的误差较大以及无法正确区分信号和噪声子空间,导致加权SL0算法的DOA估计性能恶化。针对上述问题提出了一种基于协方差匹配SL0算法的MIMO雷达DOA估计方法。该方法利用协方差匹配准则重构出一个满秩的协方差矩阵,恢复MIMO雷达协方差矩阵的Toeplitz特性,并利用协方差逆矩阵的高阶幂来近似噪声子空间从而计算加权向量。仿真分析表明,该方法能够在无需预知信源数目的情况下有效地完成对相干信号的DOA估计。 相似文献
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现有的波达方向(Direction Of Arrival,DOA)和极化参数估计方法大多基于子空间理论.本文从稀疏信号重构角度出发,提出了一种新的DOA和极化角度估计算法.该算法首先构建一个只包含DOA信息的累积量矩阵模型,然后基于加权l?1范数最小化获得DOA估计.在DOA估计的基础上,进一步通过求和平均运算构建三个包含不同极化信息的累积量向量模型,利用Zhang惩罚进行稀疏性约束,获得近似无偏的极化角度估计.阐述了如何利用极化信息来区分两个入射角度一样的信源信号.计算机仿真结果验证了所提算法的有效性. 相似文献
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一种新的近场源三维参数联合估计算法 总被引:5,自引:0,他引:5
本文研究近场源距离、频率和到达角(DOA)三维参数的联合估计问题,并提出一种计算有效的新算法。该算法利用特征值及相应的特征矢量估计信号参数,不需要谱峰搜索且各参数自动配对。此外,新算法使用四阶累积量,因此适用于任意的加性高斯噪声环境。计算机仿真证实了所提算法的有效性。 相似文献
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In this paper, we propose a novel source localization method to estimate parameters of arbitrary field sources, which may lie in near-field region or far-field region of array aperture. The proposed method primarily constructs two special spatial-temporal covariance matrixes which can avoid the array aperture loss, and then estimates the frequencies of signals to obtain the oblique projection matrixes. By using the oblique projection technique, the covariance matrixes can be transformed into several data matrixes which only contain single source information, respectively. At last, based on the sparse signal recovery method, these data matrixes are utilized to solve the source localization problem. Compared with the existing typical source localization algorithms, the proposed method improves the estimation accuracy, and provides higher angle resolution for closely spaced sources scenario. Simulation results are given to demonstrate the performance of the proposed algorithm. 相似文献
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Junli Liang Ding Liu 《Signal Processing, IEEE Transactions on》2010,58(1):108-120
Passive source localization is one of the issues in array signal processing fields. In some practical applications, the signals received by an array are the mixture of near-field and far-field sources, such as speaker localization using microphone arrays and guidance (homing) systems. To localize mixed near-field and far-field sources, this paper develops a two-stage MUSIC algorithm using cumulant. The key points of this paper are: (i) in the first stage, this paper derives one special cumulant matrix, in which the virtual ?steering vector? is the function of the common electric angle in both near-field and far-field signal models so that source direction-of-arrival (DOA) (near-field or far-field one) can be obtained from this electric angle using the conventional high-resolution MUSIC algorithm; (ii) in the second stage, this paper derives another particular cumulant matrix, in which the virtual ?steering matrix? has full column rank no matter whether the received signals are multiple near-field sources or multiple far-field ones or their mixture. What is more important, the virtual ?steering vector? can be separated into two parts, in which the first one is the function of the common electric angle in both signal models, whereas the second part is the function of the electric angle that exists only in near-field signal model. Furthermore, by substituting the common electric angle estimated in the first stage into one special Hermitian matrix formed from another MUSIC spectral function, the range of near-field sources can be obtained from the eigenvector of the Hermitian matrix. The resultant algorithm avoids two- dimensional search and pairing parameters; in addition, it avoids the estimation failure problem and alleviates aperture loss. Simulation results are presented to validate the performance of the proposed method. 相似文献
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《AEUE-International Journal of Electronics and Communications》2014,68(6):534-539
Source localization for mixed far-field and near-field sources is considered. By constructing the second-order statistics domain data of array which is only related to DOA parameters of mixed sources, we obtain the DOA estimation of all sources using the weighted ℓ1-norm minimization. And then, we use MUSIC spectral function to distinguish the mixed sources as well as to provide a more accurate DOA estimation of far-field sources. Finally, a mixed overcomplete matrix on the basis of DOA estimation is introduced in the sparse signal representation framework to estimate range parameters. The performance of the proposed method is verified by numerical simulations and is also compared with two existing methods. 相似文献
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针对嵌套阵列下近场和远场混合源定位问题,本文通过构建和训练深度展开迭代收缩阈值算法(Iterative Shrinkage Thresholding Algorithm,ISTA)网络实现混合源的波达方向(direction of arrival,DOA)和距离参数估计。首先考虑到近场源协方差矩阵具有Hermitian矩阵形式,远场源协方差矩阵具有Hermitian和Toeplitz矩阵形式,通过将混合源协方差矩阵进行差分可以得到近场源差分向量,其中近场源差分向量转换到实数域,可以显著降低深度展开ISTA网络的计算复杂度。接着将不同参数下的近场源差分向量和近场源真实空间谱进行配对作为训练样本,对近场源深度展开ISTA网络进行训练,其中深度展开ISTA网络的隐藏层对应模型驱动ISTA方法的迭代步骤。然后利用估计出的近场源DOA和距离参数,通过子空间差分方法得到远场源协方差向量。最后将不同参数下的远场源协方差向量和远场源真实空间谱进行配对作为训练样本,对远场源深度展开ISTA网络进行训练,其中远场源协方差向量同样转换到实数域。在深度展开ISTA网络的训练过程中,损失函数只与重构误差和网络输... 相似文献
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大多数的超分辨测向方法都需要掌握准确的阵列流型,然而在实际应用当中,各阵元通道对信号的幅度增益和延时往往不一致,使得真正的阵列流型和它的理论模型之间存在一定的误差,最终造成测向性能的下降.针对这个问题,论文提出了一种基于幅相误差阵列的远近场混合信号超分辨测向估计方法.首先对信号的空间谱函数进行变换判断出远场信号方向,接着根据远场信号子空间和噪声子空间的正交性估计出阵列误差并对数据进行校正,在此基础上通过矩阵分解判断出近场信号方向,同时还能够实现近场信号的定位.所提方法直接对信号空间谱函数分母的多项式求根得出信号方向和近场信号距离,回避了谱峰搜索的过程,在保证一定精度的前提下大大提高了计算速度. 相似文献
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一种新的基于压缩感知理论的稀疏信号重构算法 总被引:1,自引:4,他引:1
针对基于l1范数优化的稀疏信号重构算法需要的观测样本数较多,本文以lp范数最小化为目标,结合传统的罚函数(PF)优化思想,给出了基于PF的lp范数迭代重构算法,需要的观测样本数大大低于基于l1范数的优化计算需求,并通过数值实验表明该算法对稀疏信号具有较优的重构效果. 相似文献