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首页> 外文期刊>International journal of RF and microwave computer-aided engineering >Direction of arrival estimation with antenna arrays based on fuzzy cerebellar model articulation controller neural network
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Direction of arrival estimation with antenna arrays based on fuzzy cerebellar model articulation controller neural network

机译：基于模糊大脑模型关节控制器神经网络的天线阵列到达方向

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摘要

Direction of arrival (DOA) estimation has been a challenging problem in many applications such as wireless communication, radar, sonar, and navigation. However, it is difficult to improve the angle resolution and reduce the computational complexity of super-resolution methods. To solve these problems, the DOA estimation is viewed as a mapping problem, which can be modeled using a suitable artificial neural network trained with input-output pairs. This article presents the use of a fuzzy cerebellar model articulation controller (FCMAC) neural network for the DOA estimation under a linear antenna array. The FCMAC neural network is a special feedforward neural network based on local approximation that can be adapted to solve the multidimensional nonlinear fitting problem. A new preprocessing scheme has been used in both training and test phase. It use magnitude and phase angles instead of the real and imaginary parts of the array covariance matrix to be the input of neural network. The proposed method avoids complex matrix eigen-decomposition, such as multiple signal classification, and offers fast computation rate. The performance of FCMAC neural network is compared with the conventional subspace methods and the radial basis function neural network in the cases of noisy environment and coherent signal. Simulation results indicate that FCMAC neural network produces up to 61% lower error, 60% higher angle resolution, and 99% lower calculation time than other three methods, which indicates the superior performance of the proposed DOA estimation method under coherent signals and different noise levels.
机译：抵达方向（DOA）估计在许多应用中存在挑战性问题，例如无线通信，雷达，声纳和导航。然而，难以提高角度分辨率并降低超分辨率方法的计算复杂性。为了解决这些问题，DOA估计被视为映射问题，可以使用用输入输出对训练的合适的人工神经网络进行建模。本文介绍了在线性天线阵列下进行DOA估计的模糊小脑模型关节控制器（FCMAC）神经网络。 FCMAC神经网络是一种基于局部近似的特殊前馈神经网络，其可以适于解决多维非线性拟合问题。培训和测试阶段都使用了一种新的预处理方案。它使用幅度和相位角，而不是阵列协方差矩阵的真实和虚部，以成为神经网络的输入。所提出的方法避免复杂的矩阵eIgen分解，例如多个信号分类，并提供快速计算速率。在嘈杂环境和相干信号的情况下，与传统的子空间方法和径向基函数神经网络进行比较了FCMAC神经网络的性能。仿真结果表明，FCMAC神经网络的误差高达61％，比例分辨率为60％，计算时间比其他三种方法高达99％，这表明所提出的DOA估计方法在相干信号和不同噪声水平下的优越性。

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