共查询到17条相似文献,搜索用时 125 毫秒
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基于高阶累积量矩阵组正交联合对角化的高分辨方位估计方法 总被引:1,自引:0,他引:1
该文提出了一种基于高阶累积量矩阵组正交联合对角化的高分辨方位估计方法。该方法构造了一组高阶累积量矩阵共同来辨识阵列流型矩阵的列空间,进而进行DOA估计。并通过对高阶累积量矩阵组进行联合对角化,得到联合对角化矩阵和对角化后的矩阵组,并重新定义了空间谱。新方法可以处理相干声源,适用于有色噪声环境,且较仅使用单个高阶累积量矩阵的算法具有更高的分辨力,更低的均方根误差和更高的鲁棒性。 相似文献
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本文提出了一种基于平行因子分析的近场窄带信源频率、距离及二维到达角联合估计新算法.首先给出一种新的十字阵列;然后结合Toeplitz矩阵的结构特点,选择特定序号阵元输出计算的高阶累积量巧妙构造5个Toeplitz矩阵;接着在高阶累积量域构造平行因子分析模型,分析了该模型低秩分解的唯一性,并从其分解得到的矩阵中联合估计信源参数.与现有方法相比,该算法有效降低了阵列的孔径损失,无须参数配对.仿真结果表明该算法是有效的. 相似文献
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This paper presents a cumulant-based algorithm to achieve aperture extension for estimating the directions-of-arrival (DOAs) and the ranges of multiple Fresnel-region sources using a linear tripole array. The proposed algorithm defines two cumulant-based matrices, from which the DOA and the range of each source are estimated from the source's tripole steering vector using the ESPRIT technique. These are then used as coarse reference estimates to disambiguate the cyclic phase ambiguities induced from the spatial phase factors when the inter-sensor spacing exceeds a half wavelength. The algorithm does not require two-dimensional searching or parameter pairing, and can resolve 3(L−1) sources with L tripoles. The extension of the proposed algorithm by formulating multiple cumulant matrices and using parallel factor (PARAFAC) analysis is also presented. Simulation results are provided demonstrating the significant improvement in the performance over that of several existing algorithms. 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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当独立信号和相干信号共存时,传统四阶累积量方法无法估计出宽带相干信号的来波方向(DOA),针对这个问题提出了一种新方法。该方法首先通过离散傅里叶变换,将宽带阵列接收数据分解为若干个窄带信号,构造出各个窄带频率处的自相关矩阵,再通过MUSIC(Multiple Signal Classification)算法估计出各个窄带信号的DOA,将各个窄带信号的空间谱相加求平均,通过谱峰搜索得到宽带独立信号的DOA;然后分离出独立信号的信息,构造出一个只包含宽带相干信号信息的矩阵,最后通过稀疏重构的方法估计出相干信号的DOA。计算机仿真结果证明该算法的正确性和有效性。 相似文献
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基于高阶累积量虚拟阵列扩展的DOA估计 总被引:4,自引:0,他引:4
该文提出了一种基于高阶累积量虚拟阵列扩展的DOA估计新方法。该方法基于高阶累积量孔径扩展的性质,由实际阵元的坐标与方向矢量直接计算出虚拟阵元的坐标与方向矢量,利用两种阵元的坐标之间的关系构造四阶或六阶协方差矩阵,运用MUSIC方法对非高斯独立信号源进行DOA估计。该方法在任意阵列的情况下,对非高斯独立信号源进行一维与二维DOA估计,均能准确地估计出多于实际阵元数目的方向角与仰角。实验表明,该方法简单、有效地扩展了阵列孔径,提高了阵列的空间分辨能力,有效地抑制了高斯噪声的干扰,降低了高阶累积量协方差矩阵的计算量。 相似文献
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相干信号频率和到达角联合估计的算法 总被引:1,自引:0,他引:1
基于均匀圆阵和四阶累积量,提出了一种相干信号频率和到达角联合估计的新算法。首先,利用计算量较小的PRO-ESPRIT算法和beamspace-ESPRIT算法分别估计广义阵列响应矢量和信号的频率。然后,对广义阵列响应矢量进行模式空间变换,并利用改进的前后向线性预测方法估计出相干信号的到达角。该算法能在色噪声环境下,精确地估计出空间相干信号的频率和到达角,并且无需平滑技术和谱峰搜索,具有计算量小,参数自动配对的特点。计算机仿真结果验证了算法的有效性。 相似文献