Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field

Z. Stanković, N. Dončov, I. Milovanovic, B. Milovanovic
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引用次数: 9

Abstract

An efficient direction of arrival (DOA) estimation of multiple electromagnetic sources by using artificial neural network (ANN) approach is presented in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated and at arbitrary angular distance. The approach is based on training of the ANN in which the calculation of correlation matrix in the far-field scan area is done by using the Green function and the correlation of antenna elements feed currents used to describe stochastic sources radiation and then mapping this matrix to the space of DOA in angular coordinate. Once successfully trained, the neural network model is capable to perform an accurate DOA estimation within the training boundaries. Presented example verifies the accuracy of the proposed neural network model.
一种有效定位远场随机电磁源的神经网络模型
本文提出了一种利用人工神经网络(ANN)方法对多电磁源进行有效的到达方向估计。这里考虑的电磁源是随机辐射性的,相互不相关,在任意角距离上。该方法基于人工神经网络的训练,利用格林函数和描述随机源辐射的天线单元馈电电流的相关性计算远场扫描区域的相关矩阵,并将该矩阵映射到角坐标下的DOA空间。一旦训练成功,神经网络模型就能够在训练边界内进行精确的DOA估计。算例验证了所提神经网络模型的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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