Off-Grid DoA Estimation via Two-Stage Cascaded Neural Network
- Title
- Off-Grid DoA Estimation via Two-Stage Cascaded Neural Network
- Author
- 김선우
- Keywords
- off-grid direction-of-arrival (DoA) estimation; machine learning; cascaded neural network; convolutional neural network (CNN); deep neural network (DNN); sparse representation
- Issue Date
- 2021-01
- Publisher
- MDPI
- Citation
- ENERGIES, v. 14, no. 1, article no. 228, page. 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.
- URI
- https://www.mdpi.com/1996-1073/14/1/228https://repository.hanyang.ac.kr/handle/20.500.11754/175537
- ISSN
- 1996-1073
- DOI
- 10.3390/en14010228
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
- Files in This Item:
- Off-Grid DoA Estimation via Two-Stage Cascaded Neural Network.pdfDownload
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