Skip to main content
SpringerLink
Log in

A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. This approximation discards the term \(1/\sqrt{1 + 1/(kr_{s})^{2}}\).

  2. The Eigen-RSSI method also allows incorporation of path loss models other than the inverse square law.

  3. The SNR at which the performance of localization estimates shows a drastic reduction or “breakdown.”

  4. Note that in Figs. 1(a) and (b), the CRB increases with a decrease in SNR; however this trend is hard to observe in the figure as the value of CRB is much lower than the RMSE of the DOA estimation methods.

References

  1. A. Abdi et al., A new vector sensor receiver for underwater acoustic communication, in Oceans 2007 (IEEE Press, New York, 2007), pp. 1–10

    Chapter  Google Scholar 

  2. H. Chen, J. Zhao, Coherent signal-subspace processing of acoustic vector sensor array for DOA estimation of wideband sources. Signal Process. 85(4), 837–847 (2005)

    Article  MATH  Google Scholar 

  3. G.L. D’Spain et al., Initial analysis of the data from the vertical DIFAR array, in OCEANS 92 Proceedings—Mastering the Oceans Through Technology (IEEE Press, New York, 1992), pp. 346–351

    Chapter  Google Scholar 

  4. G.L. D’Spain et al., The simultaneous measurement of infrasonic acoustic particle velocity and acoustic pressure in the ocean by freely drifting swallow floats. IEEE J. Ocean. Eng. 16(2), 195–207 (1991)

    Article  Google Scholar 

  5. P. Felisberto et al., Tracking source azimuth using a single vector sensor, in 2010 Fourth International Conference on Sensor Technologies and Applications (2010), pp. 416–421

    Chapter  Google Scholar 

  6. V.N. Hari et al., Narrowband detection in ocean with impulsive noise using an acoustic vector sensor array, in 20th European Signal Processing Conference (EUSIPCO 2012), Bucharest, Romania (2012), pp. 1334–1339

    Google Scholar 

  7. V.N. Hari et al., Underwater signal detection in partially known ocean using short acoustic vector sensor array, in OCEANS 2011 IEEE—Spain (IEEE, Santander, 2011), pp. 1–9

    Chapter  Google Scholar 

  8. M. Hawkes, A. Nehorai, Acoustic vector-sensor beamforming and capon direction estimation. IEEE Trans. Signal Process. 46(9), 2291–2304 (1998)

    Article  Google Scholar 

  9. M. Hawkes, A. Nehorai, Acoustic vector-sensor correlations in ambient noise. IEEE J. Ocean. Eng. 26(3), 337–347 (2001)

    Article  Google Scholar 

  10. J. He, Z. Liu, Efficient underwater two-dimensional coherent source localization with linear vector-hydrophone array. Signal Process. 89(9), 1715–1722 (2009)

    Article  MATH  Google Scholar 

  11. J. He, Z. Liu, Two-dimensional direction finding of acoustic sources by a vector sensor array using the propagator method. Signal Process. 88(10), 2492–2499 (2008)

    Article  MATH  Google Scholar 

  12. H.-S. Hung et al., 3-D MUSIC with polynomial rooting for near-field source localization, in IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings (IEEE Press, New York, 1996), pp. 3065–3068

    Google Scholar 

  13. J.H. Kim et al., Passive ranging sonar based on multi-beam towed array, in OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No. 00CH37158) (IEEE Press, New York, 2000), pp. 1495–1499

    Chapter  Google Scholar 

  14. C.M. Lee et al., Efficient algorithm for localising 3-D narrowband multiple sources. IEE Proc. Radar Sonar Navig. 148(1), 23 (2001)

    Article  Google Scholar 

  15. F.W. Machell et al., Statistical characteristics of ocean acoustic noise processes, in Topics in Non-Gaussian Signal Processing, ed. by E.J. Wegman et al. (Springer, New York, 1989), pp. 29–57

    Chapter  Google Scholar 

  16. K.G. Nagananda, G.V. Anand, Subspace intersection method of high-resolution bearing estimation in shallow ocean using acoustic vector sensors. Signal Process. 90(1), 105–118 (2010)

    Article  MATH  Google Scholar 

  17. A. Nehorai, E. Paldi, Acoustic vector-sensor array processing. IEEE Trans. Signal Process. 42(9), 2481–2491 (1994)

    Article  Google Scholar 

  18. M. Porter et al., The Makai experiment: high frequency acoustics, in ECUA, Carvoerio, Portugal, ed. by S.M. Jesus, O.C. Rodríguez (2006), pp. 9–18

    Google Scholar 

  19. P. Santos et al., Geometric and seabed parameter estimation using a vector sensor array—experimental results from Makai experiment 2005, in OCEANS 2011 IEEE—Spain (IEEE Press, New York, 2011), pp. 1–10

    Chapter  Google Scholar 

  20. P. Santos et al., Seabed geoacoustic characterization with a vector sensor array. J. Acoust. Soc. Am. 128(5), 2652–2663 (2010)

    Article  Google Scholar 

  21. A. Song et al., Experimental demonstration of underwater acoustic communication by vector sensors. IEEE J. Ocean. Eng. 36(3), 454–461 (2011)

    Article  Google Scholar 

  22. A. Song et al., Time reversal receivers for underwater acoustic communication using vector sensors, in OCEANS 2008 (IEEE Press, New York, 2008), pp. 1–10

    Chapter  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  24. A.L. Swindlehurst, T. Kailath, Passive direction-of-arrival and range estimation for near-field sources, in Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling (IEEE Press, New York, 1998), pp. 123–128

    Google Scholar 

  25. P. Tichavský et al., Near-field/far-field azimuth and elevation angle estimation using a single vector hydrophone. IEEE Trans. Signal Process. 49(11), 2498–2510 (2001)

    Article  Google Scholar 

  26. A.J. Weiss, B. Friedlander, Range and bearing estimation using polynomial rooting. IEEE J. Ocean. Eng. 18(2), 130–137 (1993)

    Article  Google Scholar 

  27. K.T. Wong, Acoustic vector-sensor FFH “blind” beamforming & geolocation. IEEE Trans. Aerosp. Electron. Syst. 46(1), 444–448 (2010)

    Article  Google Scholar 

  28. K.T. Wong, M.D. Zoltowski, Closed-form underwater acoustic direction-finding with arbitrarily spaced vector hydrophones at unknown locations. IEEE J. Ocean. Eng. 22(3), 566–575 (1997)

    Article  Google Scholar 

  29. K.T. Wong, M.D. Zoltowski, Extended-aperture underwater acoustic multisource azimuth/elevation direction-finding using uniformly but sparsely spaced vector hydrophones. IEEE J. Ocean. Eng. 22(4), 659–672 (1997)

    Article  Google Scholar 

  30. K.T. Wong, M.D. Zoltowski, Root-MUSIC-based azimuth-elevation angle-of-arrival estimation with uniformly spaced but arbitrarily oriented velocity hydrophones. IEEE Trans. Signal Process. 47(12), 3250–3260 (1999)

    Article  Google Scholar 

  31. K.T. Wong, M.D. Zoltowski, Self-initiating MUSIC-based direction finding in underwater acoustic particle velocity-field beamspace. IEEE J. Ocean. Eng. 25(2), 262–273 (2000)

    Article  Google Scholar 

  32. Y.I. Wu et al., The acoustic vector-sensor’s near-field array-manifold. IEEE Trans. Signal Process. 58(7), 3946–3951 (2010)

    Article  MathSciNet  Google Scholar 

  33. Y.I. Wu, K.T. Wong, Acoustic near-field source-localization by two passive anchor-nodes. IEEE Trans. Aerosp. Electron. Syst. 48(1), 159–169 (2012)

    Article  Google Scholar 

  34. Y. Xu et al., Perturbation analysis of conjugate MI-ESPRIT for single acoustic vector-sensor-based noncircular signal direction finding. Signal Process. 87(7), 1597–1612 (2007)

    Article  MATH  Google Scholar 

  35. D. Zha, T. Qiu, Underwater sources location in non-Gaussian impulsive noise environments. Digit. Signal Process. 16(2), 149–163 (2006)

    Article  Google Scholar 

  36. X. Zhong et al., Multi-modality likelihood based particle filtering for 2-D direction of arrival tracking using a single acoustic vector sensor, in 2011 IEEE International Conference on Multimedia and Expo (IEEE Press, New York, 2011), pp. 1–6

    Google Scholar 

  37. X. Zhong et al., Particle filtering and posterior Cramér–Rao bound for 2-D direction of arrival tracking using an acoustic vector sensor. IEEE Sens. J. 12(2), 363–377 (2012)

    Article  Google Scholar 

  38. X. Zhong, A.B. Premkumar, Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor. IEEE Trans. Signal Process. 60(9), 4719–4733 (2012)

    Article  MathSciNet  Google Scholar 

  39. M.D. Zoltowski, K.T. Wong, Closed-form eigenstructure-based direction finding using arbitrary but identical subarrays on a sparse uniform cartesian array grid. IEEE Trans. Signal Process. 48(8), 2205–2210 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, CeMNet Annexe, Block N4, B2b-05, Singapore, 639798, Singapore

    V. N. Hari, A. B. Premkumar & X. Zhong

Authors
  1. V. N. Hari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. B. Premkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. X. Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. N. Hari.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hari, V.N., Premkumar, A.B. & Zhong, X. A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor. Circuits Syst Signal Process 32, 843–859 (2013). https://doi.org/10.1007/s00034-012-9508-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-012-9508-9

Keywords

Search

Navigation