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2022-02-15
Sparse Bayesian Learning Based DOA Estimation and Array Gain-Phase Error Self-Calibration
By
Progress In Electromagnetics Research M, Vol. 108, 65-77, 2022
Abstract
This paper proposes a joint estimation algorithm based on sparse-Bayesian learning (SBL) for the gain-phase problem between array antenna channels. The algorithm uses the idea of the iterative method to jointly estimate the direction-of-arrival (DOA) and gain-phase error calibration coefficients in the iterative process, combining self-calibration and calibration with a calibration source. At each iteration, the rough value of DOA is first estimated using SBL, and then the DOA estimate is used to calculate the gain-phase error calibration coefficient. The value obtained in each iteration is brought into the error cost function, which is constructed based on the principle of signal and noise subspace orthogonality. Iterations are continued until convergence to find the minimum value of the cost function. The algorithm does not require a priori knowledge of array perturbations and has good performance in DOA and array gain and phase error estimation. Simulations and experimental measurements show that the method has better calibration performance than other methods based on optimization algorithms, and the algorithm effectively improves the antenna gain.
Citation
Zili Li, and Zhao Huang, "Sparse Bayesian Learning Based DOA Estimation and Array Gain-Phase Error Self-Calibration," Progress In Electromagnetics Research M, Vol. 108, 65-77, 2022.
doi:10.2528/PIERM21123001
References

1. Xu, S., B. Chen, and H. Xiang, "Direction-of-arrival estimation using estimator banks in lowangle tracking for S-band radar," Microwave and Optical Technology Letters, Vol. 63, No. 12, 2997-3001, 2021.
doi:10.1002/mop.33020

2. McCloud, M. L. and L. L. Scharf, "A new subspace identification algorithm for high-resolution DOA estimation," IEEE Transactions on Antennas and Propagation, Vol. 50, No. 10, 1382-1390, 2002.
doi:10.1109/TAP.2002.805244

3. Vallet, P., X. Mestre, and P. Loubaton, "Performance analysis of an improved MUSIC DoA estimator," IEEE Transactions on Signal Processing, Vol. 63, No. 23, 6407-6422, 2015.
doi:10.1109/TSP.2015.2465302

4. Wang, Y., H. Chen, and Y. Peng, Theory of Spectrum Estimation, The Press of Tsinghua University, Peking, 2004.

5. Liu, S., Z. Jing, and Z. Xiao, "DOA estimation with sparse array under unknown mutual coupling," Progress In Electromagnetics Research Letters, Vol. 70, 147-153, 2017.
doi:10.2528/PIERL17081701

6. Tian, Y., Z. Dong, and S. Liu, "Direction finding for coherently distributed sources with gain-phase errors," Progress In Electromagnetics Research Letters, Vol. 96, 153-161, 2021.
doi:10.2528/PIERL20121704

7. Peng, W., et al. "A joint calibration method for array gain-phase errors and mutual coupling errors," 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), IEEE, 2020.

8. Weiss, A. J. and B. Friedlander, "Array shape calibration using sources in unknown locations - A maximum likelihood approach," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 37, No. 12, 1958-1966, 1989.
doi:10.1109/29.45542

9. Weiss, A. J. and B. Friedlander, "Eigenstructure methods for direction finding with sensor gain and phase uncertainties," Circuits, Systems and Signal Processing, Vol. 9, No. 3, 271-300, 1990.
doi:10.1007/BF01201215

10. Xu, Q., H.-H. Tao, and G.-S. Liao, "Array gain and phase error correction based on GA," Systems Engineering and Electronics, Vol. 28, No. 5, 654-657, 2006.

11. Peng, W., et al. "A joint calibration method for array gain-phase errors and mutual coupling errors," 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), IEEE, 2020.

12. Nannuru, S., et al. "Sparse Bayesian learning with multiple dictionaries," Signal Processing, Vol. 159, 159-170, 2019.
doi:10.1016/j.sigpro.2019.02.003

13. Wipf, D. P. and B. D. Rao, "Sparse Bayesian learning for basis selection," IEEE Transactions on Signal Processing, Vol. 52, No. 8, 2153-2164, 2004.
doi:10.1109/TSP.2004.831016

14. Lu, Z., et al. "Amplitude and phase errors self-correcting algorithm based on the uniform circular array," Proceedings of 2012 2nd International Conference on Computer Science and Network Technology, IEEE, 2012.

15. Xu, L., J. Chen, and Y. Gao, "Off-grid DOA estimation based on sparse representation and rife algorithm," Progress In Electromagnetics Research M, Vol. 59, 193-201, 2017.
doi:10.2528/PIERM17070404

16. Salama, A. A., M. Omair Ahmad, and M. N. S. Swamy, "Compressed sensing DOA estimation in the presence of unknown noise," Progress In Electromagnetics Research C, Vol. 102, 47-62, 2020.
doi:10.2528/PIERC20031204

17. Golub, G. H. and C. Reinsch, "Singular value decomposition and least squares solutions," Linear Algebra, 134-151, Springer, Berlin, Heidelberg, 1971.

18. Li, Y., C. Campbell, and M. Tipping, "Bayesian automatic relevance determination algorithms for classifying gene expression data," Bioinformatics, Vol. 18, No. 10, 1332-1339, 2002.
doi:10.1093/bioinformatics/18.10.1332

19. Wipf, D. P. and B. D. Rao, "An empirical Bayesian strategy for solving the simultaneous sparse approximation problem," IEEE Transactions on Signal Processing, Vol. 55, No. 7, 3704-3716, 2007.
doi:10.1109/TSP.2007.894265

20. Moon, T. K., "The expectation-maximization algorithm," IEEE Signal Processing Magazine, Vol. 13, No. 6, 47-60, 1996.
doi:10.1109/79.543975