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Cumulant-based blind optimum beamforming

机译:基于累积量的盲最优波束形成

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摘要

Sensor response, location uncertainty, and use of sample statistics can severely degrade the performance of optimum beamformers. We propose blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher order statistics. In this way, we employ the statistical characteristics of the desired signal to make the necessary discrimination, without any a-priori knowledge of array manifold and direction-of-arrival (DOA) information about the desired signal. We then improve our method to utilize the data in a more efficient manner. In any application, only sample statistics are available, so we propose a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing. We further propose a method that employs both covariance and cumulant information to combat finite sample effects. We analyze the effects of multipath propagation on the reception of the desired signal. We show that even in the presence of coherence, cumulant-based beamformer still behaves as the optimum beamformer that maximizes the signal-to-interference-plus-noise ratio (SINR). Finally, we propose an adaptive version of our algorithm simulations demonstrate the excellent performance of our approach in a wide variety of situations
机译:传感器响应,位置不确定性以及使用样本统计信息会严重降低最佳波束形成器的性能。我们建议通过高阶统计量在存在多个,定向,相关或相干高斯干扰源的情况下对源导向向量进行盲估计。这样,我们利用所需信号的统计特性来进行必要的区分,而无需任何关于阵列流形的先验知识和有关所需信号的到达方向(DOA)信息。然后,我们改进了以更有效的方式利用数据的方法。在任何应用中,只有样本统计信息可用,因此我们提出了一种稳健的波束成形方法,该方法采用了通过基于累积量的信号处理获得的转向矢量估计。我们进一步提出一种利用协方差和累积量信息来对抗有限样本效应的方法。我们分析了多径传播对所需信号接收的影响。我们表明,即使存在相干性,基于累积量的波束形成器仍然可以充当最佳的波束形成器,从而使信噪比(SINR)最大化。最后,我们提出算法仿真的自适应版本,以证明我们的方法在各种情况下的出色性能

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