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Blind closed form parameters estimation for hybrid sources

机译:混合源盲闭形参数估计

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A novel bind algorithm is proposed for ranges and direction-of-arrivals (DOAs) estimation of the hybrid narrow-band sources, which contain both the near field sources and the far field ones. To separate them, a two-step algorithm is exploited. In the first step, the far-field sources are identified by exploiting the MUSIC method. After eliminating the far field source components in the auto correlation matrix and adding the conjugate formulation, the dimensions of covariance matrix for near field sources are increased. Therefore, compared with the conventional methods, more independent near filed sources, near 4p/3 ones can be identified with a uniformly linear array (ULA) of 2p + 1 elements. Furthermore, in the near-field source estimation of our method, the peak search and pairing operations, needed in most algorithms for near field sources, can be omitted completely. Simulation results show that our ESPRIT-based near-field method provides the improved performance over conventional ones because of the dimensions increment of the covariance matrix.
机译:提出了一种新颖的绑定算法,用于混合窄带源的距离和到达方向(DOA)估计,该混合源既包含近场源又包含远场源。为了分离它们,采用了两步算法。第一步,利用MUSIC方法识别远场源。在消除了自动相关矩阵中的远场源分量并添加了共轭公式后,近场源协方差矩阵的维数增加了。因此,与常规方法相比,可以使用2p +1个元素的均匀线性阵列(ULA)来识别更独立的近场源,近4p / 3源。此外,在我们方法的近场源估计中,大多数近场源算法中所需的峰值搜索和配对操作可以完全省略。仿真结果表明,由于协方差矩阵的维数增加,我们基于ESPRIT的近场方法提供了优于常规方法的性能。

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