Improving DOA Estimation Accuracy Using Combination of U-Net Model and MUSIC Algorithm for Uniform Circular Arrays With Inactive Elements

Sang Van Doan; Nhu-Y Nguyen

doi:10.21553/rev-jec.397

Improving DOA Estimation Accuracy Using Combination of U-Net Model and MUSIC Algorithm for Uniform Circular Arrays With Inactive Elements

Sang Van Doan, Nhu-Y Nguyen

Abstract

In this study, a novel method is proposed to improve the accuracy of direction of arrival (DOA) estimation for radio signal sources when a uniform circular antenna array (UCA) has inactive elements. Specifically, the full-rank covariance matrix is reconstructed by integrating a U-Net deep learning model with the multiple signal classification (MUSIC) algorithm, even with incomplete array elements. To restore essential correlation information lost due to the inactive elements, a subspace-based full-rank recovery technique is employed. The reconstructed covariance matrix is then utilized by the MUSIC algorithm for accurate DOA estimation. Experimental results demonstrate significant improvements in accuracy, especially under low signal-to-noise ratio (SNR) conditions and with incomplete antenna arrays. Therefore, this approach ensures stable and precise DOA estimation even under non-ideal operating scenarios, offering a practical solution when antenna arrays experience element failures or physical obstructions.

Full Text:

PDF

References

H. L. Van Trees, Optimum Array Processing, Wiley-Interscience, 2002.

T. Engin Tuncer, Benjamin Friedlander, Classical and Modern Direction-of-Arrival Estimation, ELSEVIER, 2009.

A. Shaw, J. Smith, and A. Hassanien, “MVDR Beamformer Design by Imposing Unit Circle Roots Constraints for Uniform Linear Arrays,” IEEE Transactions on Signal Processing, vol. 69, pp. 6116–6130, 2021, doi: 10.1109/tsp.2021.3121630.

R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276–280, Mar. 1986, doi: 10.1109/tap.1986.1143830.

F.-M. Han and X.-D. Zhang, “An ESPRIT-like algorithm for coherent DOA estimation,” IEEE Antennas and Wireless Propagation Letters, vol. 4, pp. 443–446, 2005, doi: 10.1109/lawp.2005.860194.

Z.-M. Liu, C. Zhang, and P. S. Yu, “Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections,” IEEE Transactions on Antennas and Propagation, vol. 66, no. 12, pp. 7315–7327, Dec. 2018, doi: 10.1109/tap.2018.2874430.

Y. Mao and G. Zhang, “DOA estimation algorithm based on DFT and multiple regression,” IET Communications, vol. 14, no. 22, pp. 4127–4134, Dec. 2020, doi: 10.1049/iet-com.2020.0689.

L. Dharma Theja ch, L. Nagaraju and K. Kumar Puli “Classification based DOA estimation using ANN and CNN Models,” in 2022 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), IEEE, Dec. 2022, pp. 1470–1473. doi: 10.1109/mapcon56011.2022.10047297

DOI: http://dx.doi.org/10.21553/rev-jec.397

Copyright (c) 2025 REV Journal on Electronics and Communications

ISSN: 1859-378X

Username
Password
Remember me