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Direction of arrival estimation with antenna arrays based on fuzzy cerebellar model articulation controller neural network
Author(s) -
Tang Yuting,
Xu Shen,
Wang Xu,
Yu Jiaqiang
Publication year - 2020
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.22289
Subject(s) - artificial neural network , computer science , direction of arrival , algorithm , feedforward neural network , subspace topology , angle of arrival , sensor array , antenna (radio) , artificial intelligence , telecommunications , machine learning
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.