Skip to main content
SpringerLink
Log in

A direction finding method for spatial optical beam-forming network based on sparse Bayesian learning

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper addresses the problem of direction-of-arrival (DOA) estimation of multiple sources for spatial optical beam forming network (SOBFN). A method which utilizes sparse Bayesian learning techniques to reconstruct the spatial power spectrum of the signals is proposed. The reconstruction procedure takes advantage of the amplitude distribution of the fiber-array output and the spatial sparsity of the incident signals. The reconstructed spectrum contains some evident peaks that indicate the coarse directions of the radiation sources. Then, a linear interpolation procedure is applied to make the DOA estimation results more precise. Sufficient numerical simulations are carried out to verify the performance of the proposed algorithm. Simulation results show that the proposed method fits well with the signal model of SOBFN and has favorable DOA estimation performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Keopf, G.A.: Optical processor for phased-array beam formation. Proc. SPIE Int. Soc. Opt. Eng. 477(6), 75–81 (1984)

    Google Scholar 

  2. Konishi, Yoshihiko, Chujo, Wataru, Fujise, Masayuki: Carrier-to-noise ratio and sidelobe level in a two-laser model optically controlled array antenna using Fourier optics. IEEE Trans. Antennas Propag. 40(12), 1459–1465 (1992)

    Article  Google Scholar 

  3. Te-kao, Wu, Chandler, Charles W.: Optical beam forming techniques for phased array antennas. Int. J. lnfrared Millimeter Waves 14(6), 1191–1199 (1993)

    Article  Google Scholar 

  4. Ji, Y., Inagaki, K., Miura, R., Karasawa, Y.: Optical processor for multibeam microwave array antennas. Electron. Lett. 32(9), 822–824 (1996)

    Article  Google Scholar 

  5. Ji, Yu., Keizo, Inagaki, Osamu, Shibata: Receive mode of optical signal processing multibeam array antennas. IEEE Microw. Guided Wave Lett. 8(7), 251–253 (1998)

    Article  Google Scholar 

  6. Akiyama, Tomohiro, Inagaki, Keizo, Ohira, Takashi, Hikita, Makoto: Two-dimensional optical signal-processing beamformer using multilayer polymeric optical waveguide arrays. IEEE Trans. Microw. Theory Tech. 49(10), 2055–2061 (2001)

    Article  Google Scholar 

  7. Shibata, O., Inagaki, K.: Spatial optical beam-forming network for receiving-mode multibeam array antenna proposal and experiment. IEEE Trans. Microw. Theory Tech. 50(5), 205–208 (2002)

    Article  Google Scholar 

  8. Inagaki, K., Karasawa, Y.: Spatial optical signal processing beam-forming network for 2D beam steering. IEICE Trans. Electron. 86(7), 1209–1217 (2003)

    Google Scholar 

  9. Braker, B.M., Schlottau, F., Wagner, K.: Squint-free Fourier-optical RF beamforming using a SHB crystal as an imaging detector. IEEE J. Sel. Top. Quantum Electron. 14(3), 952–962 (2008)

    Article  Google Scholar 

  10. Liang, Wu, Zai-chen, Zhang, Hua-ping, Liu: Transmit beam-forming for MIMO optical wireless communication systems. Wirel. Pers. Commun. 78(1), 615–628 (2014)

    Article  Google Scholar 

  11. Liu-Li, Wu, Liu, Zhang-Meng, Jiang, Wen-Li: A direction of arrival estimation method for spatial optical beam-forming network. Opt. Quantum Electron. 47(8), 3041–3051 (2015)

    Article  Google Scholar 

  12. Liu, Zhang-Meng, Huang, Zhi-Tao, Zhou, Yi-Yu.: An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning. IEEE Trans. Wirel. Commun. 11(10), 1–11 (2012)

    Article  Google Scholar 

  13. Liu, Zhang-Meng, Huang, Zhi-Tao, Zhou, Yi-Yu.: Sparsity-inducing direction finding for narrowband and wideband signals based on array covariance vectors. IEEE Trans. Wirel. Commun. 12(8), 1–12 (2013)

    Article  Google Scholar 

  14. Liu, Zhang-Meng, Zhou, Yi-Yu.: 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 

  15. Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1(3), 211–244 (2001)

    MathSciNet  MATH  Google Scholar 

  16. Wipf, D.P., Rao, B.D., Nagarajan, S.: Latent variable Bayesian models for promoting sparsity. IEEE Trans. Inf. Theory 57(9), 6236–6255 (2011)

    Article  MathSciNet  Google Scholar 

  17. Malioutov, D.M., Cetin, M., Willsky, A.S.: Optimal sparse representations in general overcomplete bases. Int. Conf. Acoust. Speech Signal Process. ICASSP 2(2), ii-793–ii-796 (2004)

    Google Scholar 

  18. B, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39, 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  19. Boyles, R.A.: On the convergence of the EM algorithm. J. R. Stat. Soc. B. 45(1), 47–55 (1983)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

The work is supported by the National Natural Science Foundation of China (No. 61302141).

Author information

Authors and Affiliations

  1. Department of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, Hunan, People’s Republic of China

    Liu-li Wu, Zhang-meng Liu & Wen-li Jiang

Authors
  1. Liu-li Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang-meng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-li Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liu-li Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Ll., Liu, Zm. & Jiang, Wl. A direction finding method for spatial optical beam-forming network based on sparse Bayesian learning. SIViP 11, 203–209 (2017). https://doi.org/10.1007/s11760-016-0920-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0920-7

Keywords

Search

Navigation