DOA-CNN: An automatic system error calibration model for enhancing accuracy in direction of arrival estimation of radio frequency signals
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https://doi.org/10.54939/1859-1043.j.mst.89.2023.43-51Keywords:
DOA estimation; Convolutional neural network; Position errors; Amplitude and phase errors.Abstract
This paper presents a research proposal on a deep convolutional neural network for the problem of direction of arrival estimation of radio frequency signals (called DOA-CNN). The DOA-CNN model is designed with multiplication layers to enhance strong features of the data through convolutional stacks enabling the DOA classification accuracy. The evaluation considers several factors affecting the accuracy of DOA estimation for uniform linear array (ULA), including antenna element position errors, and amplitude and phase errors caused by transmission path deviations in the receiver. The analysis and comparison of DOA-CNN with CBF, Capon, MUSIC, Root-MUSIC, and ESPRIT methods and other machine learning methods show that, considering the ideal configuration of the ULA array and the receiver, the Root-MUSIC and ESPRIT methods achieve the best accuracy since they can directly compute the DOA, while the other methods estimate the DOA via angular spectrum, leading to accuracy dependent on the spectral resolution. However, considering ULA errors and transmission path deviations in the receiver, the proposed DOA-CNN model outperforms in terms of accuracy compared to traditional methods and processes faster than some other machine learning models.
References
. R. Wiley, “ELINT: The Interception and Analysis of Radar Signals”, Artech House, (2006).
. S. Chandran, “Advances in Direction-of-Arrival Estimation”, Artech House, (2006).
. R. Schmidt, "Multiple emitter location and signal parameter estimation," in IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276-280, (1986). DOI: https://doi.org/10.1109/TAP.1986.1143830
. Y. -M. Ning, S. Ma, F. -Y. Meng and Q. Wu, "DOA Estimation Based on ESPRIT Algorithm Method for Frequency Scanning LWA," in IEEE Communications Letters, vol. 24, no. 7, pp. 1441-1445, (2020). DOI: https://doi.org/10.1109/LCOMM.2020.2988020
. B. D. Rao and K. V. S. Hari, "Performance analysis of Root-Music," in IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 12, pp. 1939-1949, (1989). DOI: https://doi.org/10.1109/29.45540
. V.-S. Doan and D.-S. Kim, “DOA estimation of multiple non-coherent and coherent signals using element transposition of covariance matrix,” ICT Express, vol. 6, no. 2, pp. 67–75, (2020). DOI: https://doi.org/10.1016/j.icte.2020.03.008
. M. Zhang and Z.-D. Zhu, "DOA estimation with sensor gain, phase and position perturbations," Proceedings of the IEEE 1993 National Aerospace and Electronics Conference-NAECON 1993, Dayton, OH, USA, pp. 67-69 vol.1, (1993).
. L. Wu, Z.-M. Liu, and Z.-T. Huang, “Deep convolution network for direction of arrival estimation with sparse prior”, IEEE Signal Processing Letters, vol. 26, no. 11, pp. 1688–1692, (2019). DOI: https://doi.org/10.1109/LSP.2019.2945115
. S. Ge, K. Li, and S. N. B. M. Rum, “Deep Learning Approach in DOA Estimation: A Systematic Literature Review,” Mobile Information Systems, vol. 2021, pp. 1–14, (2021). DOI: https://doi.org/10.1155/2021/6392875
. V. A. Devi and M. Naved, “Dive in Deep Learning,” in Machine Learning in Signal Processing, Chapman and Hall/CRC, pp. 97–126, (2021). DOI: https://doi.org/10.1201/9781003107026-5
. A. R. Carballeira, F. A. P. de Figueiredo, and J. M. C. Brito, “Simultaneous Estimation of Azimuth and Elevation Angles Using a Decision Tree-Based Method,” (2023). DOI: https://doi.org/10.20944/preprints202307.0919.v1
. L. Jian, X. Wang, J. Shi, and X. Lan, “Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources,” Mathematics, vol. 10, no. 6, p. 923, (2022). DOI: https://doi.org/10.3390/math10060923
. Y. Liu, H. Chen, and B. Wang, “DOA Estimation of Underwater Acoustic Signals Based on PCA-kNN Algorithm,” in 2020 International Conference on Computer Information and Big Data Applications (CIBDA), (2020). DOI: https://doi.org/10.1109/CIBDA50819.2020.00115
. F. Zhao, G. Hu, C. Zhan, and Y. Zhang, “DOA Estimation Method Based on Improved Deep Convolutional Neural Network,” Sensors, vol. 22, no. 4, p. 1305, (2022). DOI: https://doi.org/10.3390/s22041305
