Abstract
In this paper, frequency-difference multiple signal classification (FD-MUSIC), a direction of arrival (DOA) method for high frequency signal source in inhomogeneous media, is proposed for an uniform linear array (ULA). In the FD-MUSIC method, the effect of inhomogeneity of media on DOA estimation results of high frequency signals with similar propagation pathes can be effectively suppressed by frequency-difference operation, so as to improve the robustness of DOA estimation. At the same time, combined with the idea of subspace decomposition in the MUSIC method, the DOA estimation azimuth resolution is improved. In order to ensure the DOA estimation performance of the FD-MUSIC method, it is required theoretically that the phase fluctuation of the frequency-difference signals caused by the inhomogeneity of media is far less than \(2\pi \), and the array element spacing of the ULA is less than half of the wavelength corresponding to the frequency-difference \(\Delta f\). Two sufficient condition for the FD-MUSIC are given in this paper and the performance of the FD-MUSIC method is analyzed by simulations. When the conditions required by the FD-MUSIC method are satisfied, simulation results show that the DOA estimation performance of the FD-MUSIC method is better than that of the conventional beamforming (CBF) method, the multiple signal classification (MUSIC) method, the frequency-difference beamforming (FDBF) method, and deconvolved frequency-difference beamforming (Dv-FDB) method in the presence of inhomogeneity.









Similar content being viewed by others
Data availability
No applicable.
References
Jin, M., Liao, G., Li, J.: Joint DOD and DOA estimation for bistatic mimo radar. Signal Process. 89(2), 244–251 (2009)
Saucan, A.A., Chonavel, T., Sintes, C., Le Caillec, J.M.: CPHD-DOA tracking of multiple extended sonar targets in impulsive environments. IEEE Trans. Signal Process. 64(5), 1147–1160 (2015)
Markhi, H.E., Haibala, M., Mrabti, F., Charge, P., Zouak, M.: An improved cyclic beamforming method for signal DOA estimation. SIViP 1(3), 267–272 (2007)
Benesty, J., Jingdong, J., Chen, H.Y.: Microphone Array Signal Processing, vol. 1. Springer, Berlin (2008)
Van Veen, B.D., Buckley, K.M.: Beamforming: a versatile approach to spatial filtering. IEEE ASSP Mag. 5(2), 4–24 (1988)
Kailath, R., Royand, T.: Esprit-estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Process. 37(7), 984–995 (1989)
Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)
Ma, F., Zhang, X.: Wideband doa estimation based on focusing signal subspace. SIViP 13, 675–682 (2019)
Ping, S.: Localized States and the Approach to Localization, pp. 243–279. Introduction to Wave Scattering, Localization and Mesoscopic Phenomena (2006)
Foroozan, F., Asif, A.: Time reversal direction of arrival estimation with cramer-rao bound analysis. In: 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, pp. 1–5. IEEE, (2010)
Gurbuz, A.C., McClellan, J.H., Cevher, V.A.: compressive beamforming method. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2617–2620. IEEE, (2008)
Zhu, W., Zhang, M., Li, P., Wu, C.: Two-dimensional doa estimation via deep ensemble learning. IEEE Access 8, 124544–124552 (2020)
Zhang, Y., Wu, Y.I.: Multiple sources localization by the wsn using the direction-of-arrivals classified by the genetic algorithm. IEEE Access 7, 173626–173635 (2019)
Chen, M., Gong, Y., Mao, X.: Deep neural network for estimation of direction of arrival with antenna array. IEEE Access 8, 140688–140698 (2020)
Liu, Z., Zhan, C., Philip, S.Y.: Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections. IEEE Trans. Antennas Propag. 66(12), 7315–7327 (2018)
Chen, P., Cao, Z., Chen, Z., Wang, X.: Off-grid DOA estimation using sparse bayesian learning in mimo radar with unknown mutual coupling. IEEE Trans. Signal Process. 67(1), 208–220 (2018)
Abadi, S.H., Rouseff, D., Dowling, D.R.: Blind deconvolution for robust signal estimation and approximate source localization. J. Acoust. Soc. Am. 131(4), 2599–2610 (2012)
Abadi, S.H., Song, H., Dowling, D.R.: Broadband sparse-array blind deconvolution using frequency-difference beamforming. J. Acoust. Soc. Am. 132(5), 3018–3029 (2012)
Ishimaru, A.: Wave Propagation and Scattering in Random Media, vol. 2. Academic press, New York (1978)
Jensen, F.B., Kuperman, W.A., Porter, M.B., Schmidt, H., Tolstoy, A.: Computational Ocean Acoustics. Springer, New York (2011)
Douglass, A.S., Dowling, D.R.: Frequency-difference beamforming in the presence of strong random scattering. J. Acoust. Soc. Am. 146(1), 122–134 (2019)
Douglass, A.S., Song, H.C., Dowling, D.: Performance comparisons of frequency-difference and conventional beamforming. J. Acoust. Soc. Am. 142(3), 1663–1673 (2017)
Xie, L., Sun, C., Tian, J.: Deconvolved frequency-difference beamforming for a linear array. J. Acoust. Soc. Am. 148(6), EL440–EL446 (2020)
Park, Y., Gerstoft, P., Lee, J.: Difference-frequency music for doas. IEEE Signal Process. Lett. 29, 2612–2616 (2022)
Lee, J., Park, Y., Gerstoft, P.: Compressive frequency-difference direction-of-arrival estimation. J. Acoust. Soc. Am. 154(1), 141–151 (2023)
Colosi, J.A.: Sound Propagation through the Stochastic Ocean. Cambridge University Press, Cambridge (2016)
Willmann-Bell, H.R., Suiter: Star Testing Astronomical Telescopes. Inc., (2001)
Brekhovskikh, L.M., Lysanov, Y.P.: Fundamentals of Ocean Acoustics, (2004)
Tournat, V., Pagneux, V., Lafarge, D., Jaouen, L.: Multiple scattering of acoustic waves and porous absorbing media. Phys. Rev. E 70(2), 026609 (2004)
Derode, A., Mamou, V., Tourin, A.: Influence of correlations between scatterers on the attenuation of the coherent wave in a random medium. Phys. Rev. E 74(3), 036606 (2006)
Millette, P.A.: The heisenberg uncertainty principle and the nyquist-shannon sampling theorem. Prog. Phys. 9(3), 9–14 (2013)
Stoica, P., Nehorai, A.: MUSIC, maximum likelihood, and cramer-rao bound. IEEE Trans. Acoust. Speech Signal Process. 37(5), 720–741 (1989)
Zhang, Z., Shi, Z., Gu, Y.: Ziv-zakai bound for doas estimation. IEEE Trans. Signal Process. 71, 136–149 (2022)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 52071309 and 52001296, the Taishan Scholars under Grant No. tsqn 201909053, and the Fundamental Research Funds for Central Universities under Grant Nos. 202161003, 202065005, 862001013102, 202165007.
Author information
Authors and Affiliations
Contributions
Conceptualization, W.G. and S.Z.; methodology, X.L.; software, S.Z., X.L, W.G; validation, X.L., H.W. and L.W.; formal analysis, S.Z. and X.L; investigation, H.W. and L.W.; resources, S.Z. W.G.; data curation, S.Z.; writing-original draft preparation, X. L. and W.G.; writing-review and editing, S.Z., H.W. and L.W.; visualization, S.Z., X.L. and W.G.; supervision, L.W., H. W. and X.L.; project administration, W.G. and X.L. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no Conflict of interest.
Ethical approval
No applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gao, W., Zhu, S., Li, X. et al. Frequency-difference MUSIC: a method for DOA estimation in inhomogeneous media. SIViP 18, 7029–7040 (2024). https://doi.org/10.1007/s11760-024-03372-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-024-03372-1