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Propagator-based computationally efficient direction finding via low-dimensional equation rooting

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Abstract

Direction-of-arrival (DOA) estimation of multiple emitters with sensor arrays has been a hot topic in the area of signal processing during the past decades. Among the existing DOA estimation methods, the subspace-based ones have attracted a lot of research interest, mainly due to their satisfying performance in direction estimation precision and super-resolution of temporally overlapping signals. However, subspace-based DOA estimation methods usually contain procedures of covariance matrix decomposition and refined spatial searching, which are computationally much demanding and significantly deteriorate the computational efficiency of these methods. Such a drawback in heavy computational load of the subspace-based methods has further blocked the application of them in practical systems. In this paper, we follow the major process of the subspace-based methods to propose a new DOA estimation algorithm, and devote ourselves to reduce the computational load of the two procedures of covariance matrix decomposition and spatial searching, so as to improve the overall efficiency of the DOA estimation method. To achieve this goal, we first introduce the propagator method to realize fast estimation of the signal-subspace, and then establish a DOA-dependent characteristic polynomial equation (CPE) with its order equaling the number of incident signals (which is generally much smaller than that of array sensors) based on the signal-subspace estimate. The DOA estimates are finally obtained by solving the low-dimensional CPE. The computational loads of both the subspace estimation and DOA calculation procedures are thus largely reduced when compared with the corresponding procedures in traditional subspace-based DOA estimation methods, e.g., MUSIC. Theoretical analyses and numerical examples are carried out to demonstrate the predominance of the proposed method in both DOA estimation precision and computational efficiency over existing ones.

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References

  1. Han, Y., Tran, V.: Recursive Bayesian beamforming with uncertain projected steering vector and strong interferences. Signal Image Video Process 10(5), 975–982 (2016)

    Article  Google Scholar 

  2. Tao, J., Fan, Q., Yu, F.: Stokes parameters and DOAs estimation of partially polarized sources using a EM vector sensor. Signal Image Video Process. 10(8), 1–8 (2016)

    Google Scholar 

  3. Ta, S., Wang, H., Chen, H.: Two-dimensional direction-of-arrival estimation and pairing using L-shaped arrays. Signal Image Video Process. 10(8), 1511–1518 (2016)

    Article  Google Scholar 

  4. Wu, L., Liu, Z., Jiang, W.: A direction finding method for spatial optical beam-forming network based on sparse Bayesian learning. Signal Image Video Process. 11(2), 203–209 (2017)

    Article  Google Scholar 

  5. Capon, J.: High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE 57(8), 1408–1418 (1969)

    Article  Google Scholar 

  6. Marzetta, T.L.: A new interpretation for Capon’s maximum likelihood method of frequency-wavenumber spectrum estimation. IEEE Trans. Acoust. Speech Signal Process. 31, 445–449 (1983)

    Article  Google Scholar 

  7. Burg, J.P.: Maximum entropy spectral analysis. In: Proceeding of 37th Annual International SEG meeting, Oklahoma City (1967)

  8. Capon, J.: Maximum-likelihood spectral estimation. In: Haykin, S. (ed.) Nonlinear Methods of Spectral Analysis, pp. 155–179. Springer, New York (1979)

    Chapter  Google Scholar 

  9. Cox, H., Zeskind, R.M., Owen, M.M.: Robust adaptive beamforming. IEEE Trans. Acoust. Speech Signal Process. ASSP 35, 1365–1376 (1987)

    Article  Google Scholar 

  10. Li, J., Stoica, P., Wang, Z.: On robust Capon beamforming and diagonal loading. IEEE Trans. Signal Process. 51(7), 1702–1715 (2003)

    Article  Google Scholar 

  11. Stoica, P., Wang, Z., Li, J.: Robust Capon beamforming. IEEE Signal Process. Lett. 10(6), 172–175 (2003)

    Article  Google Scholar 

  12. Somasundaram, S.D.: Linearly constrained robust Capon beamforming. IEEE Trans. Signal Process. 60(11), 5845–5856 (2012)

    Article  MathSciNet  Google Scholar 

  13. Schmidt, R.O.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. AP 34, 276–280 (1986)

    Article  Google Scholar 

  14. Barabell, A.J.: Improving the resolution performance of eigenstructure-based direction-finding algorithms. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Boston, MA, pp. 336–339 (1983)

  15. Roy, R., Kailath, T.: ESPRIT—estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Process. 37(7), 984–995 (1989)

    Article  MATH  Google Scholar 

  16. Rao, B.D., Hari, K.V.S.: Performance analysis of Root-MUSIC. IEEE Trans. Acoust. Speech Signal Process. 37(12), 1939–1949 (1989)

    Article  Google Scholar 

  17. Stoica, P., Nehorai, A.: MUSIC, maximum likelihood, and Cramer–Rao bound. IEEE Trans. Acoust. Speech Signal Process. 37(5), 720–741 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  18. Wang, H., Kaveh, M.: On the performance of signal-subspace processing—part I: narrow-band systems. IEEE Trans. Acoust. Speech Signal Process. 34(5), 1201–1209 (1986)

    Article  Google Scholar 

  19. Mestre, X., Lagunas, M.A.: Modified subspace algorithms for DoA estimation with large arrays. IEEE Trans. Signal Process. 56(2), 598–614 (2008)

    Article  MathSciNet  Google Scholar 

  20. Liu, Z.-M., Huang, Z.-T., Zhou, Y.-Y.: Direction-of-arrival estimation of wideband signals via covariance matrix sparse representation. IEEE Trans. Signal Process. 59(9), 4256–4270 (2011)

    Article  MathSciNet  Google Scholar 

  21. Liu, Z.-M., Huang, Z.-T., Zhou, Y.-Y.: An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning. IEEE Trans. Wireless Commun. 11(10), 3607–3617 (2012)

    Google Scholar 

  22. Liu, Z.-M., Zhou, Y.-Y.: A unified framework and sparse Bayesian perspective for direction-of-arrival estimation in the presence of array imperfections. IEEE Trans. Signal Process. 61(15), 3786–3798 (2013)

    Article  MathSciNet  Google Scholar 

  23. Malioutov, D., Cetin, M., Willsky, A.S.: A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. Signal Process. 53(8), 3010–3022 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. Hyder, M.M., Mahata, K.: Direction-of-arrival estimation using a mixed L2,0 norm approximation. IEEE Trans. Signal Process. 58(9), 4646–4655 (2010)

    Article  MathSciNet  Google Scholar 

  25. Liu, Z.-M., Huang, Z.-T., Zhou, Y.-Y.: Sparsity-inducing direction finding for narrowband and wideband signals based on array covariance vectors. IEEE Trans. Wireless Commun. 12(8), 3896–3907 (2013)

    Article  Google Scholar 

  26. Wang, L., Zhao, L., Bi, G., Wan, C., Zhang, L., Zhang, H.: Novel wideband DOA estimation based on sparse Bayesian learning with dirichlet process priors. IEEE Trans. Signal Process. 64(2), 275–289 (2016)

    Article  MathSciNet  Google Scholar 

  27. Hawes, M., Mihaylova, L., Septier, F., Godsill, S.: Bayesian compressive sensing approaches for direction of arrival estimation with mutual coupling effects. IEEE Trans. Antennas Propag. 65(3), 1357–1368 (2017)

    Article  MathSciNet  Google Scholar 

  28. Liu, Z.-M., Guo, F.-C.: Azimuth and elevation estimation with rotating long-baseline interferometers. IEEE Trans. Signal Process. 63(9), 2405–2419 (2015)

    Article  MathSciNet  Google Scholar 

  29. Marcos, S., Marsal, A., Benidir, M.: The propagator method for source bearing estimation. Sig. Process. 42, 121–138 (1995)

    Article  Google Scholar 

  30. Tayem, N., Kwon, H.M.: L-shape 2-dimensional arrival angle estimation with propagator method. IEEE Trans. Antennas Propag. 53(5), 1622–1630 (2005)

    Article  Google Scholar 

  31. Shu, T., Liu, X., Lu, J.: Comments on L-shape 2-dimensional arrival angle estimation with propagator method. IEEE Trans. Antennas Propag. 56(5), 1502–1503 (2008)

    Article  Google Scholar 

  32. Stoica, P., Sharman, K.C.: Novel eigenanalysis method for direction estimation. IEE Proc. F Radar Signal Process. 137(1), 19–26 (1990)

    Article  MathSciNet  Google Scholar 

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Shiying, T., Hongqiang, W. Propagator-based computationally efficient direction finding via low-dimensional equation rooting. SIViP 12, 83–90 (2018). https://doi.org/10.1007/s11760-017-1133-4

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