Off-Grid DoA Estimation via Two-Stage Cascaded Neural Network

Hyeonjin Chung, Hyeongwook Seo, Jeungmin Joo, Dongkeun Lee and Sunwoo Kim
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Hyeonjin Chung: Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea
Hyeongwook Seo: Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea
Jeungmin Joo: Agency for Defense Development, Yuseong P.O. Box 35, Daejeon 34186, Korea
Dongkeun Lee: Agency for Defense Development, Yuseong P.O. Box 35, Daejeon 34186, Korea
Sunwoo Kim: Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea

Energies, 2021, vol. 14, issue 1, 1-11

Abstract: This paper introduces an off-grid DoA estimation via two-stage cascaded network which can resolve a mismatch between true direction-of-arrival (DoA) and discrete angular grid. In the first-stage network, the initial DoAs are estimated with a convolutional neural network (CNN), where initial DoAs are mapped on the discrete angular grid. To deal with the mismatch between initially estimated DoAs and true DoAs, the second-stage network estimates a tuning vector which represents the difference between true DoAs and nearest discrete angles. By using tuning vector, the final DoAs are estimated by moving initially estimated DoAs as much as the difference between true DoAs and nearest discrete angles. The limitation on estimation accuracy induced by the discrete angular grid can be resolved with the proposed two-stage network so that the estimation accuracy can be further enhanced. Simulation results show that adding the second-stage network after the first-stage network helps improve the estimation accuracy by resolving mismatch induced by the discretized grid. In the aspect of the implementation of machine learning, results also show that using CNN and using PReLU as the activation function is the best option for accurate estimation.

Keywords: off-grid direction-of-arrival (DoA) estimation; machine learning; cascaded neural network; convolutional neural network (CNN); deep neural network (DNN); sparse representation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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