Abstract
As neural network algorithm springs up, it is being applied to the DOA estimation more and more often. However, the robustness and real-time performance of the neural network algorithm under the condition of the multiple sources has always been a difficult problem. The DOA estimation algorithm of the neural network based on interval division has better robustness and real-time performance, which divides the signal into different angle intervals. However, the accuracy of signal division is difficult to be guaranteed, especially when the angle of signal is at interval edge, causing the large error. An improved interval division method is thus proposed in this paper. Firstly, by adjusting the weight of the antenna array, the beam of the antenna array is focused at the angle corresponding to each sub-region, therefore the spatial signal feature is improved, In the interval division, the edge overlapping division is adopted to improve the estimation accuracy of the interval edge angle. After experimental verification, the improved interval partition method can improve the performance of the algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Naidu, P.S.: Sensor Array Signal Processing. CRC Press, Inc. (2000)
Schmidt, R.O.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)
Roy, R., Kailath, T.: ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Process. 37(7), 984–995 (1989)
Krim, H., Viberg, M.: Two decades of array signal processing research: the parametric approach. IEEE Signal Process. Mag. 13(4), 67–94 (1996)
Huang, H., Yang, J., Huang, H., et al.: Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans. Veh. Technol. 67(9), 8549–8560 (2018)
Wang, Z.Q., Zhang, X., Wang, D.L.: Robust speaker localization guided by deep learning-based time-frequency masking. IEEE/ACM Transactions on Audio, Speech, and Language Processing 27(1), 178–188 (2019)
Zhu, C., Zhu, L.: A DOA estimation algorithm of satellite interference signals based on machine learning. Radio Commun. Technol. 45(06), 586–590 + 585 (2019)
Advance, S., Politis, A., Virtanen, T.: Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network. In: 2018 26th European Signal Processing Conference (EUSIPCO), Rome, pp. 1462–1466 (2018)
Nguyen, T.N.T., Gan, W.-S., Ranjan, R., Jones, D.L.: Robust source counting and DOA estimation using spatial pseudo-spectrum and convolutional neural network. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28, 2626–2637 (2020). https://doi.org/10.1109/TASLP.2020.3019646
Li, S.: Research of DOA estimation based on neural network. Harbin Institute of Technology (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, G., Song, X., Shan, K. (2021). An Improved DOA Estimation Algorithm of Neural Network Based on Interval Division. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-030-75078-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-75078-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75077-0
Online ISBN: 978-3-030-75078-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)