Low Complexity DFT Based DOA Estimation for Synthetic Nested Array Using Single Moving Sensor

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Abstract

The issue of direction of arrival (DOA) estimation for synthetic nested array is investigated in this paper. The synthetic nested array (SNA) is formed by one single sensor moving according to the configuration of the physical nested array. With the synthetic array, both high resolution DOA estimation and array aperture miniaturization requirements can be met. To reduce the computationally complexity for SNA, a discrete Fourier transform (DFT) based algorithm is proposed which needs no eigen decomposition. We first reconstruct the data matrix reshaped from the data received by moving senor to obtain the observation vector and then get the initial DOA estimates via DFT of the observation vector. At last the fine estimates can be obtained through searching for peaks corrected by phase rotation matrix over a small sector. The proposed algorithm for SNA can achieve better bearing estimation performance than spatial smoothing (SS) subspace based methods such as SS-MUSIC and SS-ESPRIT, due to the fact that it can fully utilize array aperture while SS-MUSIC and SS-ESPRIT lose a half. Besides, the proposed algorithm involves full degree of freedoms (DOF). Numerical simulations validate the efficiency and superiority of the proposed algorithm.

Keywords

Synthetic array Nested array Direction of arrival Low complexity DFT 

Notes

Acknowledgements

This work is supported by China NSF Grants (61371169, 61601167, 61601504), Jiangsu NSF (BK20161489), the open research fund of State Key Laboratory of Millimeter Waves, Southeast University (No. K201826), the Fundamental Research Funds for the Central Universities (No. NE2017103) and Graduate Innovative Base (laboratory) Open Funding of Nanjing University of Aeronautics and Astronautics (kfjj20170412).

References

  1. 1.
    Krim, H., & Viberg, M. (1996). Two decades of array signal processing research: The parametric approach. IEEE Signal Processing Magazine, 13(4), 67–94.CrossRefGoogle Scholar
  2. 2.
    Zhang Xiaofei, W. F., & Dazhuan, Xue. (2010). Theory and application of array signal processing. Beijing: National Defense Industry Press.MATHGoogle Scholar
  3. 3.
    Jianpeng, W. (2011). Moving array signal processing technology based on spatial and temporal extending. Changsha: National University of Defense Technology.Google Scholar
  4. 4.
    Hao, X. C., Xie, S. G., Zeng, X. Y., Du, X., & Wang, C. (2016). A near-field radiation source localization method based on passive synthetic arrays using single channel receiver. In IEEE international symposium on microwave, antenna, propagation, and EMC technologies (pp. 31–35).Google Scholar
  5. 5.
    Al-Ardi, E. M., Shubair, R. M., & Al-Mualla, M. E. (2006). Direction of arrival estimation in a multipath environment: An overview and a new contribution. Applied Computational Electromagnetics Society Journal, 21(3), 226–238.Google Scholar
  6. 6.
    Sheinvald, J., Wax, M., & Meiss, A. J. (1997). Localization of multiple sources with moving arrays. IEEE Transactions on Signal Processing, 46(10), 2736–2743.CrossRefGoogle Scholar
  7. 7.
    Williams, R., & Harris, B. (1992). Passive acoustic synthetic aperture processing techniques. IEEE Journal of Oceanic Engineering, 17(1), 8–15.CrossRefGoogle Scholar
  8. 8.
    Xie, D., Niu, T., Huang, J., & Ge, H. (2008).Maximum likelihood parameters estimation in non-uniform noise fields using moving array. In 42nd Asilomar conference on signals, systems and computers (pp. 1732–1735).Google Scholar
  9. 9.
    Autrey, S. W. (1988). Passive synthetic arrays. Journal of the Acoustical Society of America, 84(84), 592–598.CrossRefGoogle Scholar
  10. 10.
    Sullivan, E. J. (2000). On the role of modeling in passive synthetic aperture processing. In OCEANS 2000 MTS/IEEE conference and exhibition (Vol 1, pp. 7–9).Google Scholar
  11. 11.
    Stergiopoulos, S. (1990). Optimum bearing resolution for a moving towed array and extension of its physical aperture. Journal of the Acoustical Society of America, 87(5), 2128–2140.CrossRefGoogle Scholar
  12. 12.
    Stergiopoulos, S., & Urban, H. (2002). A new passive synthetic aperture technique for towed arrays. IEEE Journal of Oceanic Engineering, 17(1), 16–25.CrossRefGoogle Scholar
  13. 13.
    Gorban, I. I. (2000). Space–time signal processing for moving antennae. Advances in Engineering Software, 31(2), 119–125.CrossRefMATHGoogle Scholar
  14. 14.
    Edelson, G. S., & Tufts, D. W. (1992). On the ability to estimate narrow-band signal parameters using towed arrays. IEEE Journal of Oceanic Engineering, 17(1), 48–61.CrossRefGoogle Scholar
  15. 15.
    Ramirez, J., Odom, J., & Krolik, J. (2014). Exploiting array motion for augmentation of co-prime arrays. In Sensor array and multichannel signal processing workshop (pp. 525–528).Google Scholar
  16. 16.
    Ramirez, J., & Krolik, J. (2015). Multiple source localization with moving co-prime arrays. In IEEE international conference on acoustics, speech and signal processing (pp. 2374–2378).Google Scholar
  17. 17.
    Demissie, B., Oispuu, M., & Ruthotto, E. (2008). Localization of multiple sources with a moving array using subspace data fusion. In International conference on information fusion (pp. 1–7).Google Scholar
  18. 18.
    Keller, D. R., Moon, T. K., & Gunther, J. H. (2006). Narrowband source localization from a moving array of sensors. In 40th Asilomar conference on signals, systems and computers (pp. 2285–2289).Google Scholar
  19. 19.
    Ke, Z., Peng, M., & Zhang, J. Y. (2011) DOA estimation algorithm based on FFT in switch antenna array. In IEEE CIE international conference on radar (pp. 1425–1428).Google Scholar
  20. 20.
    See, C. M. S. (2003). A single channel approach to high resolution direction finding and beamforming. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (vol. 215, pp. V-217–220).Google Scholar
  21. 21.
    Roy, R., & Kailath, T. (2002). ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(7), 984–995.CrossRefMATHGoogle Scholar
  22. 22.
    Zhang, X., Xu, L., Xu, L., & Xu, D. (2010). Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC. IEEE Communications Letters, 14(12), 1161–1163.CrossRefGoogle Scholar
  23. 23.
    Cao, R., Liu, B., Gao, F., & Zhang, X. (2017). A low-complex one-snapshot DOA estimation algorithm with massive ULA. IEEE Communications Letters, 21(5), 1071–1074.CrossRefGoogle Scholar
  24. 24.
    Liu, C.-L., & Vaidyanathan, P. (2017). Cramér–Rao bounds for coprime and other sparse arrays, which find more sources than sensors. Digital Signal Processing, 61, 43–61.CrossRefGoogle Scholar
  25. 25.
    Pal, P., & Vaidyanathan, P. P. (2010). Nested arrays: A novel approach to array processing with enhanced degrees of freedom. IEEE Transactions on Signal Processing, 58(8), 4167–4181.MathSciNetCrossRefGoogle Scholar
  26. 26.
    Pal, P., & Vaidyanathan, P. P. (2012). Multiple level nested array: An efficient geometry for 2qth order cumulant based array processing. IEEE Transactions on Signal Processing, 60(3), 1253–1269.MathSciNetCrossRefGoogle Scholar
  27. 27.
    Shan, T. J., Wax, M., & Kailath, T. (1985). On spatial smoothing for direction-of-arrival estimation of coherent signals. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(4), 806–811.CrossRefGoogle Scholar
  28. 28.
    Rao, B., & Hari, K. V. S. (1990). Effect of spatial smoothing on state space methods/ESPRIT. In 5th ASSP workshop on spectrum estimation and modeling (pp. 377–381).Google Scholar
  29. 29.
    Stoica, P., & Arye, N. (1990). MUSIC, maximum likelihood, and Cramer–Rao bound. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(5), 720–741.MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Zeira, A., & Friedlander, B. (1995). Direction finding with time-varying arrays. IEEE Transactions on Signal Processing, 43(4), 927–937.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

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