Hybrid ANN model for accurate 2D DOA estimation of a radiating source

M. Agatonovic, Z. Stanković
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引用次数: 4

Abstract

Given that simulation models may often suffer from reduced accuracy when applied to real environmental conditions, in this paper we propose a hybrid model for two-dimensional direction of arrival (2D DOA) estimation of a radiating source. The model is based on artificial neural networks (ANNs), and its development is conducted in two phases. Initially, an ANN is trained to predict angular positions of a simulated radiating source in a certain range of azimuth and elevation angles. The second phase includes development of a corrective empirical ANN aimed to improve the accuracy of the simulation-based network. Finally, the hybrid ANN model is able to account for real environmental conditions and physical aspects of the receiving antenna array. The performance of the model is verified by measurements for several positions of the transmitting antenna.
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基于混合神经网络模型的辐射源二维方位精确估计
考虑到仿真模型在实际环境条件下往往存在精度降低的问题,本文提出了一种用于辐射源二维到达方向(2D DOA)估计的混合模型。该模型基于人工神经网络(ann),其发展分为两个阶段。首先,训练人工神经网络来预测模拟辐射源在一定方位和仰角范围内的角度位置。第二阶段包括纠正经验人工神经网络的发展,旨在提高基于仿真的网络的准确性。最后,混合人工神经网络模型能够考虑实际环境条件和接收天线阵列的物理方面。通过对发射天线多个位置的测量,验证了该模型的有效性。
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