Abstract
In this paper, a novel convolutional neural network (CNN) was designed for DOA estimation, which could deploy in radio-electronics systems for improving the accuracy and operation efficiency. The proposed model was evaluated with different hyper-parameter configurations for optimization, and then a suitable model was compared with other existing models to demonstrate its preeminence. Regarding dataset generation, our work considered the influence of both Gaussian noise and multipath channels to DOA estimation accuracy. According to the analysis, in frame of this study, the model with 5 conv-blocks, 48 filters, and a filter size of \( 1\times 7 \) achieved the best performance in terms of accuracy (\(75.27\%\) at \(+5\) dB SNR) and prediction time (10.1 ms) that notably outperformed two other state-of-the-art CNN model-based DOA estimation techniques.
Supported by ICTCRC, Kumoh National Institute of Technology, South Korea.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34, 276–280 (1986)
Roy, R., Kailath, T.: ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Process. 37, 984–995 (1989)
Reddy, K.M., Reddy, V.U.: Analysis of spatial smoothing with uniform circular arrays. IEEE Trans. Signal Process. 47, 1726–1730 (1999)
Pillai, S.U., Kwon, B.H.: Forward/backward spatial smoothing techniques for coherent signal identification. IEEE Trans. Acoust. Speech Signal Process. 37, 8–15 (1989)
Lin, J.-D., Fang, W.-H., Wu, M.-L.: Joint spatial-temporal channel parameter estimation using tree-structured MUSIC. In: IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367) (2002)
Wang, Q., Chen, H., Zhao, G., Chen, B., Wang, P.: An improved direction finding algorithm based on Toeplitz approximation. Sensors 13, 746–757 (2013)
Pastorino, M., Randazzo, A.: A smart antenna system for direction of arrival estimation based on a support vector regression. IEEE Trans. Antennas Propag. 53, 2161–2168 (2005)
Gonnouni, A.E., Martinez-Ramon, M., Rojo-Alvarez, J.L., Camps-Valls, G., Figueiras-Vidal, A.R., Christodoulou, C.G.: A support vector machine MUSIC algorithm. IEEE Trans. Antennas Propag. 60(10), 4901–4910 (2012)
Sun, F.-Y., Tian, Y.-B., Hu, G.-B., Shen, Q.-Y.: DOA estimation based on support vector machine ensemble. Int. J. Numer. Model. Electron. Netw. Dev. Fields, e2614 (2019)
Huang, H., Yang, J., Huang, H., Song, Y., Gui, G.: Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans. Veh. Technol. 67(9), 8549–8560 (2018)
Liu, Z.-M., Zhang, C., Yu, P.S.: Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections. IEEE Trans. Antennas Propag. 66(12), 7315–7327 (2018)
Adavanne, S., Politis, A., Virtanen, T.: Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network, CoRR, vol. abs/1710.10059 (2017)
Chakrabarty, S., Habets, E.A.P.: Multi-speaker DOA estimation using deep convolutional networks trained with noise signals. IEEE J. Sel. Top. Signal Process. 13(1), 8–21 (2019)
Wu, L., Liu, Z.-M., Huang, Z.-T.: Deep convolution network for direction of arrival estimation with sparse prior. IEEE Signal Process. Lett. 26(11), 1688–1692 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Huynh-The, T., Hua, C.-H., Pham, Q.-V., Kim, D.-S.: MCNet: an efficient CNN architecture for robust automatic modulation classification. IEEE Commun. Lett. 24(4), 811–815 (2020)
Huynh-The, T., Hua, C., Kim, J., Kim, S., Kim, D.: Exploiting a low-cost CNN with skip connection for robust automatic modulation classification. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, Seoul, Korea (South) (2020)
Doan, V.-S., Kim, D.-S.: DOA estimation of multiple non-coherent and coherent signals using element transposition of covariance matrix. ICT Express 6(2), 67–75 (2020)
Acknowledgment
This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea (NRF-2019K2A9A1A09081533) and Priority Research Centers Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (NRF-2018R1A6A1A03024003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Doan, VS., Huynh-The, T., Hoang, VP., Kim, DS. (2020). Convolutional Neural Network-Based DOA Estimation Using Non-uniform Linear Array for Multipath Channels. In: Vo, NS., Hoang, VP. (eds) Industrial Networks and Intelligent Systems. INISCOM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-030-63083-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-63083-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63082-9
Online ISBN: 978-3-030-63083-6
eBook Packages: Computer ScienceComputer Science (R0)