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Array processing in correlated noise fields based on instrumental variables and subspace fitting

机译:基于工具变量和子空间拟合的相关噪声场中的阵列处理

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摘要

Accurate signal parameter estimation from sensor array data is a problem which has received much attention in the last decade. A number of parametric estimation techniques have been proposed in the literature. In general, these methods require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. This difficulty can be overcome only by introducing alternative assumptions that enable separating the signals from the noise. In some applications, the raw sensor outputs can be preprocessed so that the emitter signals are temporally correlated with correlation length longer than that of the noise. An instrumental variable (IV) approach can then be used for estimating the signal parameters without knowledge of the spatial color of the noise. A computationally simple IV approach has recently been proposed by the authors. Herein, a refined technique that can give significantly better performance is derived. A statistical analysis of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities. A lower bound on the attainable error variance is also presented. The proposed optimal IV method is shown to attain the bound if the signals have a quasideterministic character.
机译:根据传感器阵列数据进行准确的信号参数估计是一个问题,在过去的十年中,这一问题备受关注。文献中已经提出了许多参数估计技术。通常,这些方法需要了解噪声的传感器之间的相关性,这构成了明显的缺点。只有通过引入使信号与噪声分离的替代假设,才能克服这一困难。在某些应用中,可以对原始传感器的输出进行预处理,以使发射器信号在时间上具有比噪声长的相关长度。然后,可以使用工具变量(IV)方法来估计信号参数,而无需了解噪声的空间颜色。作者最近提出了一种计算简单的IV方法。在此,推导了可以给出明显更好的性能的改进技术。对参数估计值进行统计分析,从而可以最佳地选择某些用户指定的数量。还给出了可达到的误差方差的下限。如果信号具有准确定性特征,则建议的最佳IV方法将达到边界。

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