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Fast DOA estimation algorithm based on a combination of an orthogonal projection and noise pseudo-eigenvector approach. (English) Zbl 1214.94024

Summary: This paper presents a new fast direction of arrival (DOA) estimation technique, using both the projection spectrum and the eigenspectrum. First, the rough DOA range is selected using the projection spectrum; then, a linear matrix equation is used to acquire a noise pseudo-eigenvector. Finally, the fine DOA estimation is obtained from an eigenspectrum approach based on the noise pseudo-eigenvector. Without the need to form the covariance matrix from a block of the array data and without a prior knowledge of the number of incoming signals, reduced complexity is achieved, in contrast to conventional subspace-based algorithms. Simulation results show that the proposed algorithm has a good resolution performance and deals well with both uncorrelated and correlated signals. Since the new approach can reduce computational complexity while maintaining better or similar resolution capability, it may provide wider application prospects in real-time DOA estimation when contrasted to other comparable methods.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)
Full Text: DOI

References:

[1] R. O. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276-280, 1986.
[2] A. Paulraj, A. Roy, and T. Kailath, “Estimation of signal parameters via rotational invariance techniques-esprit,” in Proceedings of the 19th Asilomar Conference on Circuits, Systems and Computers, pp. 83-89, November 1985.
[3] W. Choi, T. K. Sarkar, H. Wang, and E. L. Mokole, “Adaptive processing using real weights based on a direct data domain least squares approach,” IEEE Transactions on Antennas and Propagation, vol. 54, no. 1, pp. 182-190, 2006. · doi:10.1109/TAP.2005.859753
[4] K. Kim, T. K. Sarkar, H. Wang, and M. Salazar-Palma, “Direction of arrival estimation based on temporal and spatial processing using a direct data domain (D) approach,” IEEE Transactions on Antennas and Propagation, vol. 52, no. 2, pp. 533-541, 2004. · doi:10.1109/TAP.2004.823994
[5] K. Kim, T. K. Sarkar, and M. S. Palma, “Adaptive processing using a single snapshot for a nonuniformly spaced array in the presence of mutual coupling and near-field scatterers,” IEEE Transactions on Antennas and Propagation, vol. 50, no. 5, pp. 582-590, 2002. · doi:10.1109/TAP.2002.1011223
[6] T. K. Sarkar, J. Koh, R. Adve et al., “A pragmatic approach to adaptive antennas,” IEEE Antennas and Propagation Magazine, vol. 42, no. 2, pp. 39-55, 2000.
[7] T. K. Sarkar, et al., Smart Antennas, IEEE Press, New York, NY, USA; Wiley-Interscience, Hoboken, NJ, USA, 2003.
[8] J. T. Kim, S. H. Moon, D. S. Han, and M. J. Cho, “Fast DOA estimation algorithm using pseudocovariance matrix,” IEEE Transactions on Antennas and Propagation, vol. 53, no. 4, pp. 1346-1351, 2005. · doi:10.1109/TAP.2005.844459
[9] Z. Wen, L.-P. Li, and P. Wei, “Fast direction finding using modified pseudocovariance matrix,” IEEE Transactions on Antennas and Propagation, vol. 54, no. 12, pp. 3914-3918, 2006. · doi:10.1109/TAP.2006.886572
[10] G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins University Press, Baltimore, Md, USA, 1983. · Zbl 0559.65011
[11] R. Grover, D. A. Pados, and M. J. Medley, “Subspace direction finding with an auxiliary-vector basis,” IEEE Transactions on Signal Processing, vol. 55, no. 2, pp. 758-763, 2007. · Zbl 1391.94226 · doi:10.1109/TSP.2006.885771
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