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Tracking with MIMO sonar systems: applications to harbour surveillance

Tracking with MIMO sonar systems: applications to harbour surveillance

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Multiple-input multiple-output (MIMO) sonar systems offer new perspectives for area surveillance especially in complex environments where strong multipath and dense clutter can become very challenging. This study proposes a MIMO sonar system based scheme to tackle the difficult problem of harbour surveillance. An emphasis is put on recognition and tracking on low-profile mid-water targets. First, a MIMO simulator which can compute synthetic raw data for any transmitter/ receiver pair in a multipath, cluttered and dynamic environment is presented. The study then proposes two radically different methods for the underwater target tracking problem in complex environment: a digital tracker and an analogue tracker. On the digital side, an implementation of the recently developed hypothesised filter for independent stochastic populations is presented. This filter enables robust multi-object tracking as well as track classification capabilities without the use of heuristics. An analogue filter based on acoustical time reversal techniques is also introduced. This filter directly uses the returned acoustic field from the scene to focus the sound on the expected target position, hence improving the signal-to-noise ratio on the target in complex environments and taking full advantage of the MIMO architecture.

References

    1. 1)
      • 44. Panta, K., Vo, B.-N., Singh, S., et al: ‘Probability hypothesis density filter versus multiple hypothesis tracking’. Defense and Security, Int. Society for Optics and Photonics, 2014, pp. 284295.
    2. 2)
      • 16. Pailhas, Y., Houssineau, J., Delande, E., et al: ‘Tracking underwater objects using large MIMO sonar systems’. Proc. of Int. Conf. on Underwater Acoustics (UA2014), Rhodes, Greece, June 2014, pp. 10331040.
    3. 3)
      • 30. Fink, M., Prada, C., Wu, F., et al: ‘Self focusing in inhomogeneous media with time reversal acoustic mirrors’. Proc. of Ultrasonics Symp., October 1989, pp. 681686.
    4. 4)
      • 12. Fewell, M., Ozols, S.: ‘Simple detection-performance analysis of multistatic sonar for anti-submarine warfare’. Technical Report, DSTO Defence Science and Technology Organisation, 2011.
    5. 5)
      • 45. Clark, D.E., Panta, K., Vo, B.-N.: ‘The GM-PHD filter multiple target tracker’. 9th Int. Conf. on Proc. of Information Fusion, 2006, 2006, pp. 18.
    6. 6)
      • 46. Clark, D.E., Bell, J.: ‘Bayesian multiple target tracking in forward scan sonar images using the PHD filter’, IET Radar Sonar Navig., 2005, 152, pp. 327334.
    7. 7)
      • 28. Houssineau, J., Clark, D.E., Del Moral, P.: ‘A sequential Monte Carlo approximation of the HISP filter’. Proc. of European Signal Processing Conf. (EUSIPCO), Nice, France, 2015.
    8. 8)
      • 19. Mahler, R.P.S.: ‘Multitarget Bayes filtering via first-order multitarget moments’, IEEE Trans. Aerosp. Electron. Syst., 2003, 39, (4), pp. 11521178.
    9. 9)
      • 37. Williams, K., Jackson, D.: ‘Bistatic bottom scattering: model, experiments, and model/data comparison’. Technical Report, APL-UW, 1997.
    10. 10)
      • 17. Pailhas, Y., Petillot, Y.: ‘Large MIMO sonar systems: a tool for underwater surveillance’. Proc. of Sensor Signal Processing for Defence (SSPD), Edinburgh, UK, September 2014, pp. 15.
    11. 11)
      • 47. Vo, B.-N., Ma, W.-K.: ‘The Gaussian mixture probability hypothesis density filter’, IEEE Trans. Signal Process., 2006, 54, (11), pp. 40914104.
    12. 12)
      • 43. Home, A., Yates, G.: ‘Bistatic synthetic aperture radar’. IEEE RADAR, 2002, pp. 610.
    13. 13)
      • 15. Pailhas, Y., Petillot, Y., Mulgrew, B., et al: ‘Spatially distributed MIMO sonar systems: principles and capabilities’, IEEE J. Ocean. Eng., 2016, doi: 10.1109/JOE.2016.2593602.
    14. 14)
      • 36. Kaplan, L., Kuo, C.-C.: ‘An improved method for 2-D self-similar image synthesis’, IEEE Trans. Image Process., 1996, pp. 754761.
    15. 15)
      • 3. Juan, Y., Feng, X., Jia, L., et al: ‘The reverberation suppression in wideband diver detection sonar’. Proc. of IEEE OCEANS Conf., St. Johns, September 2014, pp. 14.
    16. 16)
      • 32. Song, H., Kuperman, W., Hodgkiss, W., et al: ‘Demonstration of a high-frequency acoustic barrier with a time-reversal mirror’, IEEE J. Ocean. Eng., 2003, 28, (2), pp. 246249.
    17. 17)
      • 41. Lurton, X.: ‘An introduction to underwater acoustics, principles and applications’ (Springer-Verlag Berlin Heidelberg, 2010).
    18. 18)
      • 38. Etter, P.C.: ‘Underwater acoustic modeling and simulation’ (Spon Press, 2003).
    19. 19)
      • 11. Kim, S., Ku, B., Hong, W., et al: ‘Performance comparison of target localization for active sonar systems’, IEEE Trans. Aerosp. Electron. Syst., 2008, 44, (4), pp. 13711380.
    20. 20)
      • 5. DeMarco, K.J., West, M.E., Howard, A.M.: ‘Sonar-based detection and tracking of a diver for underwater human–robot interaction scenarios’. Proc. of 2013 IEEE Int. Conf. on Systems, Man, and Cybernetics, October 2013, pp. 23782383.
    21. 21)
      • 48. Pollard, D.: ‘A user's guide to measure theoretic probability’ (Cambridge University Press, 2002), vol. 8.
    22. 22)
      • 9. Sharma, N.S., Yakubovskiy, A.M., Zimmerman, M.J.: ‘Scuba diver detection and classification in active and passive sonars: a unified approach’. Proc. of Technologies for Homeland Security (HST), November 2013, pp. 189194.
    23. 23)
      • 2. Kessel, R.T.: ‘NATO harbour protection trials 2006 (HPT06) analyst report: harbour surveillance systems’. Technical Report NURC-FR-2007-004, NATO Undersea Research Centre, 2007.
    24. 24)
      • 25. Smith, D., Singh, S.: ‘Approaches to multisensor data fusion in target tracking: a survey’, IEEE Trans. Knowl. Data Eng., 2006, 18, (12), pp. 16961710.
    25. 25)
      • 1. Kessel, R.T., Hollett, R.D.: ‘Underwater intruder detection sonar for harbour protection: state of the art review and implications’. Technical Report NURC-PR-2006-027, NATO Undersea Research Centre, 2006.
    26. 26)
      • 18. Blackman, S.S.: ‘Multiple hypothesis tracking for multiple target tracking’, IEEE Trans. Aerosp. Electron. Syst. Mag., 2004, 19, (1), pp. 518.
    27. 27)
      • 4. Xinke, L., Zhengxiang, X.: ‘Underwater small target tracking algorithm based on diver detection sonar image sequences’. Proc. of Industrial Control and Electronics Engineering (ICICEE), August 2012, pp. 727730.
    28. 28)
      • 21. Erkmen, B., Yildirim, T.: ‘Improving classification performance of sonar targets by applying general regression neural network with PCA’, Expert Syst. Appl., 2008, 35, (2), pp. 472475.
    29. 29)
      • 34. Roux, P., Akal, T., Hodgkiss, W.S., et al: ‘Time-reversal arrays in underwater acoustics’, J. Acoust. Soc. Am., 2004, 116, (4), pp. 25262526.
    30. 30)
      • 33. Sabra, K.G., Roux, P., Song, H.-C., et al: ‘Experimental demonstration of iterative time-reversed reverberation focusing in a rough waveguide. Application to target detection’, J. Acoust. Soc. Am., 2006, 120, (3), pp. 13051314.
    31. 31)
      • 35. Mandelbrot, B.: ‘The fractal geometry of nature’ (W H Freeman & Co, 1982).
    32. 32)
      • 10. Hari, V.N., Chitre, M., Too, Y.M., et al: ‘Robust passive diver detection in shallow ocean’. Proc. of IEEE OCEANS Conf., Genova, May 2015, pp. 16.
    33. 33)
      • 14. Orlando, D., Ehlers, F.: ‘Advances in multistatic sonar’, in Kolev, N.Z. (Ed.): ‘Sonar systems’ (InTech, 2011), pp. 2950.
    34. 34)
      • 8. Fillinger, L., Hunter, A.J., Zampolli, M., et al: ‘Passive acoustic detection of closed-circuit underwater breathing apparatus in an operational port environment’, J. Acoust. Soc. Am., 2012, 132, (4), pp. EL310EL316.
    35. 35)
      • 7. Stolkin, R., Florescu, I.: ‘Probabilistic analysis of a passive acoustic diver detection system for optimal sensor placement and extensions to localization and tracking’. Proc. of IEEE OCEANS Conf., September 2007, pp. 16.
    36. 36)
      • 49. Schuhmacher, D., Vo, B.-T., Vo, B.-N.: ‘A consistent metric for performance evaluation of multi-object filters’, IEEE Trans. Signal Process., 2008, 56, (8), pp. 34473457.
    37. 37)
      • 23. Ayrulu, B., Barshan, B.: ‘Neural networks for improved target differentiation and localization with sonar’, Neural Netw., 2001, 14, (3), pp. 355373.
    38. 38)
      • 24. Perry, S.W., Guan, L.: ‘A recurrent neural network for detecting objects in sequences of sector-scan sonar images’, IEEE J. Ocean. Eng., 2004, 29, (3), pp. 857871.
    39. 39)
      • 50. Schlangen, I., Franco, J., Houssineau, , et al: ‘Marker-less stage drift correction in super-resolution microscopy using the single-cluster PHD filter’, IEEE J. Sel. Top. Signal Process., 2016, 10, (1), pp. 193202.
    40. 40)
      • 26. Winter, M., Favier, G.: ‘A neural network for data association’. Proc. of Acoustics, Speech, and Signal Processing, Phoenix, USA, March 1999, pp. 10411044.
    41. 41)
      • 6. Chen, X., Wang, R., Tureli, U.: ‘Passive acoustic detection of divers under strong interference’. Proc. of IEEE OCEANS Conf., September 2006, pp. 16.
    42. 42)
      • 39. de Sousa Costa, E., Bauzer Medeiros, E., Carvalho Filardi, J.B.: ‘Underwater acoustics modeling in finite depth shallow waters’, in Beghi, M.G. (Ed): ‘Modeling and measurement methods for acoustic waves and for acoustic microdevices’ (InTech, 2013).
    43. 43)
      • 20. Mahler, R.P.S.: ‘PHD filters of higher order in target number’, IEEE Trans. Aerosp. Electron. Syst., 2007, 43, (4), pp. 15231543.
    44. 44)
      • 31. Prada, C., Thomas, J., Fink, M.: ‘The iterative time reversal process: analysis of the convergence’, J. Acoust. Soc. Am., 1994, 97, (1), pp. 6271.
    45. 45)
      • 29. McKenna, I., Tonolini, F., Tobin, R., et al: ‘Observing the dynamics of waterborne pathogens for assessing the level of contamination’. Proc. of Sensor Signal Processing for Defence Conf., Edinburgh, UK, 2015.
    46. 46)
      • 22. Shams, S.: ‘Neural network optimization for multi-target multi-sensor passive tracking’, Proc. IEEE, 1996, 84, (10), pp. 14421457.
    47. 47)
      • 13. Ehlers, F.: ‘Final report on deployable multistatic sonar systems’. Technical Report, NATO Undersea Research Centre, 2009.
    48. 48)
      • 40. Porter, M.B., Bucker, H.P.: ‘Gaussian beam tracing for computing ocean acoustic fields’, J. Acoust. Soc. Am., 1987, 82, (4), pp. 13491359.
    49. 49)
      • 42. Pailhas, Y., Petillot, Y., Capus, C., et al: ‘Target detection using statistical MIMO’. Proc. of Meetings on Acoustics, Edinburgh, UK, 2012.
    50. 50)
      • 27. Houssineau, J.: ‘Representation and estimation of stochastic populations’. PhD thesis, Heriot-Watt University, 2015.
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