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http://dx.doi.org/10.12673/jant.2020.24.1.47

Three Stage Neural Networks for Direction of Arrival Estimation  

Park, Sun-bae (Department of Electronic and Electrical Engineering, Hongik University)
Yoo, Do-sik (Department of Electronic and Electrical Engineering, Hongik University)
Publication Information
Journal of Advanced Navigation Technology / v.24, no.1, 2020 , pp. 47-52 More about this Journal
Abstract
Direction of arrival (DoA) estimation is a scheme of estimating the directions of targets by analyzing signals generated or reflected from the targets and is used in various fields. Artificial neural networks (ANN) is a field of machine learning that mimics the neural network of living organisms. They show good performance in pattern recognition. Although researches has been using ANNs to estimate the DoAs, there are limitationsin dealing with variations of the signal-to-noise ratio (SNR) of the target signals. In this paper, we propose a three-stage ANN algorithm for DoA estimation. The proposed algorithm can minimize the performance reduction by applying the model trained in a single SNR environment to various environments through a 'noise reduction process'. Furthermore, the scheme reduces the difficulty in learning and maintains efficiency in estimation, by employing a process of DoA shift. We compare the performance of the proposed algorithm with Cramer-Rao bound (CRB) and the performances of existing subspace-based algorithms and show that the proposed scheme exhibits better performance than other schemes in some severe environments such as low SNR environments or situations in which targets are located very close to each other.
Keywords
Artificial neural network; DoA estimation; Multi layer perceptron; Signal processing; Subspace-based methods;
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