2018-08-23
Wideband Direction of Arrival Estimation Based on the Principal Angle Between Subspaces
By
Progress In Electromagnetics Research Letters, Vol. 78, 23-29, 2018
Abstract
In this paper, we propose a novel method for wideband direction of arrival (DOA) estimation. By calculating the largest principal angle between the signal subspace and the subspace spanned by the augmented array manifold, the proposed method can estimate direction of arrival of wideband signals. Unlike conventional wideband methods, it adopts a new augmented array manifold and constructs the augmented matrix entirely by processing the received signals in frequency domain. It does not require any preliminary DOA estimates or focusing matrices. Simulation results show that the proposed method exhibits satisfactory performance at medium and high signal-to-noise ratio (SNR) conditions in comparison to the existing wideband DOA estimation methods.
Citation
Zhiyu Feng, Hongshu Liao, Lu Gan, Dong Yang, and Rong Hu, "Wideband Direction of Arrival Estimation Based on the Principal Angle Between Subspaces," Progress In Electromagnetics Research Letters, Vol. 78, 23-29, 2018.
doi:10.2528/PIERL18060407
References

1. Van Trees, H. L., Optimum Array Processing: Part IV of Detection, Estimation and Modulation Theory, Vol. 1, Wiley Online Library, 2002.

2. Doron, M. A., A. J. Weiss, and H. Messer, "Maximum-likelihood direction finding of wide-band sources," IEEE Transactions on Signal Processing, Vol. 41, No. 1, 411, 1993.
doi:10.1109/TSP.1993.193166        Google Scholar

3. Wax, M., T.-J. Shan, and T. Kailath, "Spatio-temporal spectral analysis by eigenstructure methods," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 32, No. 4, 817-827, 1984.
doi:10.1109/TASSP.1984.1164400        Google Scholar

4. Wang, H. and M. Kaveh, "Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 33, No. 4, 823-831, 1985.
doi:10.1109/TASSP.1985.1164667        Google Scholar

5. Valaee, S. and P. Kabal, "Wideband array processing using a two-sided correlation transformation," IEEE Transactions on Signal Processing, Vol. 43, No. 1, 160-172, 1995.
doi:10.1109/78.365295        Google Scholar

6. Swingler, D. N. and J. Krolik, "Source location bias in the coherently focused high-resolution broad-band beamformer," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 37, No. 1, 143-145, 1989.
doi:10.1109/29.17516        Google Scholar

7. Di Claudio, E. D. and R. Parisi, "Waves: Weighted average of signal subspaces for robust wideband direction finding," IEEE Transactions on Signal Processing, Vol. 49, No. 10, 2179-2191, 2001.
doi:10.1109/78.950774        Google Scholar

8. Pal, P. and P. Vaidyanathan, "A novel autofocusing approach for estimating directions-of-arrival of wideband signals," 2009 Conference Record of the Forty-Third Asilomar Conference on IEEE Signals, Systems and Computers, 1663-1667, 2009.
doi:10.1109/ACSSC.2009.5469796        Google Scholar

9. Yoon, Y.-S., L. M. Kaplan, and J. H. McClellan, "Tops: New DOA estimator for wideband signals," IEEE Transactions on Signal Processing, Vol. 54, No. 6, 1977-1989, 2006.
doi:10.1109/TSP.2006.872581        Google Scholar

10. Mahata, K., "A subspace algorithm for wideband source localization without narrowband filtering," IEEE Transactions on Signal Processing, Vol. 59, No. 7, 3470-3475, 2011.
doi:10.1109/TSP.2011.2135856        Google Scholar

11. Yan, F.-G., M. Jin, S. Liu, and X.-L. Qiao, "Real-valued music for efficient direction estimation with arbitrary array geometries," IEEE Transactions on Signal Processing, Vol. 62, 1548-1560, 2014.
doi:10.1109/TSP.2014.2298384        Google Scholar

12. Yan, F.-G., Y. Shen, and M. Jin, "Fast doa estimation based on a split subspace decomposition on the array covariance matrix," Signal Processing, Vol. 115, 1-8, 2015.
doi:10.1016/j.sigpro.2015.03.008        Google Scholar

13. Huang, J., Q. Huang, L. Zhang, and Y. Fang, "A real-valued approach for wideband DOA estimation using spherical arrays," Signal Processing, Vol. 125, No. C, 79-86, 2016.
doi:10.1016/j.sigpro.2016.01.009        Google Scholar

14. Shi, J., Q. F. Zhang, and Y. Wang, "Wideband DOA estimation based on A-shaped array," IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 1-5, 2017.        Google Scholar

15. Hu, N., B. Sun, J. J. Wang, and J. F. Yang, "Covariance-based DOA estimation for wideband signals using joint sparse Bayesian learning," IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 1-5, 2017.        Google Scholar

16. Golub, G. H. and C. F. van Loan, Matrix Computations, 374-426, Johns Hopkins University Press, 1996.

17. Wax, M. and T. Kailath, "Detection of signals by information theoretic criteria," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 33, No. 2, 387-392, 1985.
doi:10.1109/TASSP.1985.1164557        Google Scholar