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Two-dimensional direction finding of coherent signals with a linear array of vector hydrophones

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Abstract

In this paper, we propose a parallel factor (PARAFAC) analysis based two dimensional direction finding algorithm for coherent signals using a uniformly linear array of vector hydrophones. By forming a PARAFAC model using spatial signature of vector hydrophone array, the new algorithm requires neither spatial smoothing nor vector-field smoothing to decorrelate the signal coherency. We also establish that the azimuth-elevation angles of K coherent signals can be uniquely determined by PARAFAC analysis, as long as the number of hydrophones \(L \ge 2K - 1\). In addition, because the vector hydrophone array manifold contains no phase information, this new algorithm can offer high azimuth-elevation estimation accuracy by setting vector hydrophones to space much farther apart than a half-wavelength. Simulation results are finally presented to verify the efficacy of the proposed algorithm.

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References

  • Carroll, J. D., & Chang, J. J. (1970). Analysis of individual differences in multidimensional scaling via an N-way generalization of Eckart-Young decomposition. Psychometrika, 35(3), 283–319.

    Article  MATH  Google Scholar 

  • Harshman, R. A. (1970). Foundations of the PARAFAC procedure: Models and conditions for an ‘explanatory’ multi-modal factor analysis. UCLA Working Papers in Phonetics, 16, 1–84.

    Google Scholar 

  • He, J., Jiang, S. L., Wang, J. T., & Liu, Z. (2010). Particle-velocity-field difference smoothing for coherent source localization in spatially nonuniform noise. IEEE Journal of Oceanic Engineering, 35(1), 113–119.

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  • He, J., & Liu, Z. (2009). Computationally efficient underwater acoustic 2-D source localization with arbitrarily spaced vector hydrophones at unknown locations using the propagator method. Multidimensional Systems and Signal Processing, 20(3), 285–296.

    Article  MATH  Google Scholar 

  • Kruskal, J. B. (1977). Three-way arrays: Rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics. Linear Algebra and its Applications, 18, 95–138.

    Article  MATH  MathSciNet  Google Scholar 

  • Nehorai, A., & Paldi, E. (1994). Acoustic vector sensor array processing. IEEE Transactions on Signal Processing, 42(9), 2481–2491.

    Article  Google Scholar 

  • Shan, T. J., Wax, M., & Kailath, T. (1985). On spatial smoothing for estimation of coherent signals. IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP–33(4), 806–811.

    Article  Google Scholar 

  • Sidiropoulos, N. D., Bro, R., & Giannakis, G. B. (2000). Parallel factor analysis in sensor array processing. IEEE Transactions on Signal Processing, 48(8), 2377–2388.

    Article  Google Scholar 

  • Sidiropoulos, N. D., Giannakis, G. B., & Bro, R. (2000). Blind PARAFAC receivers for DS-CDMA systems. IEEE Transactions on Signal Processing, 48(3), 810–823.

    Article  Google Scholar 

  • Tao, J., Chang, W., & Cui, W. (2007). Vector field smoothing for DOA estimation of coherent underwater acoustic signals in presence of a reflecting boundary. IEEE Sensors Journal, 7(8), 1152–1158.

    Article  Google Scholar 

  • Tao, J., Chang, W., & Shi, Y. (2008). Direction-finding of coherent sources via ‘particle-velocity-field smoothing’. IET Radar, Sonar & Navigation, 2, 127–134.

    Article  Google Scholar 

  • Tichavsky, P., Wong, K. T., & Zoltowski, M. D. (2001). Near-field/far-field azimuth and elevation angle estimation using a single vector hydrophone. IEEE Transactions on Signal Processing, 49(11), 2498–2510.

    Article  Google Scholar 

  • Wang, H., & Liu, K. J. R. (1998). 2-D spatial smoothing for multipath coherent signal separation. IEEE Transactions on Aerospace and Electronic Systems, 34(2), 391–405.

    Article  MathSciNet  Google Scholar 

  • Wong, K. T., & Zoltowski, M. D. (1997). Closed-form underwater acoustic direction-finding with arbitrarily spaced vector hydrophones at unknown locations. IEEE Journal of Oceanic Engineering, 22(3), 566–575.

    Article  Google Scholar 

  • Wong, K. T., & Zoltowski, M. D. (1997). Extended-aperture underwater acoustic multi-source azimuth/elevation direction-finding using uniformly but sparsely spaced vector hydrophones. IEEE Journal of Oceanic Engineering, 22(4), 659–672.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wong, K. T., & Zoltowski, M. D. (2000). Self-initiating velocity-field beamspace music for underwater acoustic direction-finding with irregularly spaced vector hydrophones. IEEE Journal of Oceanic Engineering, 25(2), 262–273.

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

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Correspondence to Ting Shu.

Appendix

Appendix

In this appendix, we will show that for an vector hydrophone, every two vector hydrophone response vectors with distinct azimuth-elevation angles are linear independent. For the problem under consideration, the values of \(\theta _k\) and \(\phi _k\) are confined as \(\theta _k \in [0, \pi /2)\) and \(\phi _k \in [0, 2\pi )\). Since \(\mathbf{c}_k = \mathbf{c}_{\ell }\) if \(\theta _k = \theta _{\ell } = 0\), regardless of the values of \(\phi _k\) and \(\phi _{\ell }\). Thus, the azimuth-elevation angles of the \(k\hbox {th}\) and the \(\ell \hbox {th}\) signals are distinct if

$$\begin{aligned} \left\{ \begin{array}{ll} (\theta _k, \phi _k) \ne (\theta _{\ell }, \phi _{\ell }) &{} \mathrm{if} \ \theta _k \ne 0 \\ \theta _k \ne \theta _{\ell } &{} \mathrm{if} \ \theta _k = 0 \\ \end{array} \right. \end{aligned}$$

Next, consider the following cases

  1. 1.

    \(\theta _1 = 0\), \(\phi _2 \in \{\pi /2, 3\pi /2\}\). In this case, the determinant of the matrix

    $$\begin{aligned} \mathrm{det}\left[ \begin{array}{cc} 1 &{}\quad 1 \\ \sin \theta _1 \sin \phi _1 &{}\quad \sin \theta _2 \sin \phi _2 \\ \end{array} \right] \ne 0 \end{aligned}$$

    Therefore, \(\mathbf{c}_1\) and \(\mathbf{c}_2\) are linear independent.

  2. 2.

    \(\theta _1 = 0\), \(\phi _2 \in \{0, \pi \}\). In this case, the determinant of the matrix

    $$\begin{aligned} \mathrm{det}\left[ \begin{array}{cc} 1 &{}\quad 1 \\ \sin \theta _1 \cos \phi _1 &{}\quad \sin \theta _2 \cos \phi _2 \\ \end{array} \right] \ne 0 \end{aligned}$$

    Therefore, \(\mathbf{c}_1\) and \(\mathbf{c}_2\) are linear independent.

  3. 3.

    \(\theta _1 \ne 0\), \(\theta _2 \ne 0\), \(\phi _2 \ne m\pi /2, m = 0, 1, 2, 3\), \(\phi _2 = \phi _1 + n \pi , n = 0, 1\). In this case, the determinant of the matrix

    $$\begin{aligned} \mathrm{det}\left[ \begin{array}{cc} 1 &{}\quad 1 \\ \sin \theta _1 \cos \phi _1 &{}\quad \sin \theta _2 \cos \phi _2 \\ \end{array} \right] \ne 0 \end{aligned}$$

    Therefore, \(\mathbf{c}_1\) and \(\mathbf{c}_2\) are linear independent.

  4. 4.

    \(\theta _1 \ne 0\), \(\theta _2 \ne 0\), \(\phi _2 \ne m\pi /2, m = 0, 1, 2, 3\), \(\phi _2 \ne \phi _1 + \pi \). In this case, the determinant of the matrix

    $$\begin{aligned} \mathrm{det}\left[ \begin{array}{cc} \sin \theta _1 \cos \phi _1 &{}\quad \sin \theta _2 \cos \phi _2 \\ \sin \theta _1 \sin \phi _1 &{}\quad \sin \theta _2 \sin \phi _2 \\ \end{array} \right] = \sin \theta _1 \sin \theta _2 (\cos \phi _1 \sin \phi _2 - \sin \phi _1 \cos \phi _2) \ne 0 \end{aligned}$$

    Therefore, \(\mathbf{c}_1\) and \(\mathbf{c}_2\) are linear independent.

Finally, we combine all the above results and conclude that for all distinct \((\theta _1, \phi _1)\) and \((\theta _2, \phi _2)\), the vectors \(\mathbf{c}_1\) and \(\mathbf{c}_2\) are linear independent.

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Wang, K., He, J., Shu, T. et al. Two-dimensional direction finding of coherent signals with a linear array of vector hydrophones. Multidim Syst Sign Process 28, 293–304 (2017). https://doi.org/10.1007/s11045-016-0399-y

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  • DOI: https://doi.org/10.1007/s11045-016-0399-y

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