volume | PIER Journals

Abstract

Aiming at the problem of look direction error in the desired signal, a novel robust adaptive beamforming method based on covariance matrix reconstruction is proposed. Firstly, the Sparse Bayesian Learning (SBL) is performed to acquire the true signal direction and the spatial spectrum simultaneously. Secondly, the SBL spatial spectrum is used to reconstruct the interference-plus-noise covariance matrix. Compared with other reconstruction algorithms, this approach can realize the position estimation without any optimization procedures. Theoretical analysis, simulation results and water pool experiments demonstrate the effectiveness and robustness of the propose algorithm.

1. Gu, Y. and A. Leshem, "Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation," IEEE Trans. Signal Process., Vol. 60, No. 7, 3881-3885, 2012.
doi:10.1109/TSP.2012.2194289 Google Scholar

2. Huang, L., J. Zhang, X. Xu, and Z. Ye, "Robust adaptive beamforming with a novel interference-plus-noise covariance matrix reconstruction method," IEEE Trans. Signal Process., Vol. 63, No. 7, 1643-1650, 2015.
doi:10.1109/TSP.2015.2396002 Google Scholar

3. Gu, Y., N. A. Goodman, S. Hong, and Y. Li, "Robust adaptive beamforming based on interference covariance matrix sparse reconstruction," Signal Processing, Vol. 96, Part B, 375-381, 2014.
doi:10.1016/j.sigpro.2013.10.009 Google Scholar

4. Zhang, Z., W. Liu, W. Leng, A. Wang, and H. Shi, "Interference-plus-noise covariance natrix reconstruction via spatial power spectrum sampling for robust adaptive beamforming," IEEE Signal Process. Lett., Vol. 23, No. 1, 121-125, 2015.
doi:10.1109/LSP.2015.2504954 Google Scholar

5. Zheng, Z., Y. Zheng, W. Wang, and H. Zhang, "Covariance matrix reconstruction with interference steering vector and power estimation for robust adaptive beamforming," IEEE Transactions on Vehicular Technology, Vol. 67, No. 9, 8495-8503, 2018.
doi:10.1109/TVT.2018.2849646 Google Scholar

6. Zhu, X., X. Xu, and Z. Ye, "Robust adaptive beamforming via subspace for interference covariance matrix reconstruction," Signal Processing, Vol. 167, 107289.1-107289.10, 2020. Google Scholar

7. Huang, J., H. Su, and Y. Yang, "Robust adaptive beamforming method based on desired signal steering vector estimation and interference-plus-noise covariance matrix reconstruction," The Journal of Engineering, Vol. 2019, No. 21, 7683-7686, 2019.
doi:10.1049/joe.2019.0739 Google Scholar

8. Trees, H. L., Detection, Estimation, and Modulation Theory, Optimum Array Processing, Wiley-Interscience, 2002.

9. Gerstoft, P., C. F. Mecklenbrauker, A. Xenaki, et al. "Multisnapshot sparse bayesian learning for DOA," IEEE Signal Process. Lett., Vol. 23, No. 10, 1469-1473, 2016.
doi:10.1109/LSP.2016.2598550 Google Scholar

10. Mecklenbrauker, C. F., P. Gerstoft, A. Panahi, et al. "Sequential Bayesian sparse signal reconstruction using array data," IEEE Trans. Signal Process., Vol. 61, No. 24, 6344-6354, 2013.
doi:10.1109/TSP.2013.2282919 Google Scholar

11. Giri, R. and B. Rao, "Type I and type II Bayesian methods for sparse signal recovery using scale mixtures," IEEE Trans. Signal Process., Vol. 64, No. 13, 3418-3428, 2016.
doi:10.1109/TSP.2016.2546231 Google Scholar

12. Raj, A. G. and J. H. McClellan, "Single snapshot super-resolution DOA estimation for arbitrary array geometries," IEEE Signal Process. Lett., Vol. 26, No. 1, 119-123, 2019.
doi:10.1109/LSP.2018.2881927 Google Scholar

13. Zhang, Z. and B. D. Rao, "Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning," IEEE J. Sel. Topics Signal Process., Vol. 5, No. 5, 912-926, 2011.
doi:10.1109/JSTSP.2011.2159773 Google Scholar

14. Liu, Z.-M., Z.-T. Huang, and Y.-Y. Zhou, "An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning," IEEE Trans. Wireless Commun., Vol. 11, No. 10, 1-11, 2012.
doi:10.1109/TWC.2012.090312.111912 Google Scholar

15. Zhang, J. A., Z. Chen, P. Cheng, and X. Huang, "Multiple-measurement vector based implementation for single-measurement vector sparse Bayesian learning with reduced complexity," Signal Processing, Vol. 118, 153-158, 2016.
doi:10.1016/j.sigpro.2015.06.020 Google Scholar

16. Zhang, Z., T.-P. Jung, S. Makeig, et al. "Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals," IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 22, No. 6, 1186-1197, 2014.
doi:10.1109/TNSRE.2014.2319334 Google Scholar

17. Jaffer, A. G., "Maximum likelihood direction finding of stochastic sources: A separable solution," Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2893-2896, 2002. Google Scholar

18. Stoica, P. and R. Moses, Introduction to Spectral Analysis, Prentice Hall, 1997.

Dr. Yang Bi

Dr. Xi'an Feng

Dr. Tuo Guo