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Real-time Uncertainty Output for MBES Systems

This paper explores the quantification of random errors in range or depth measurements for multibeam echo sounder (MBES) systems. It presents a methodology for adapting existing error models to MBES-specific parameters and validates the models through experimental tests in different environments. The study highlights the need for real-time output that measures environmental conditions and suggests further tuning of the error models.

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Real-time Uncertainty Output for MBES Systems

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  1. Real-time Uncertainty Output for MBES Systems Eric Maillard, George Yufit, Pawel PocwiardowskiRESON, Inc Eric.Maillard@Reson.com

  2. Introduction • What uncertainty? • Imperfect Sonar in a perfect world • Sound speed assumed perfectly known • No refraction correction • … • Quantify random error in range or depth measurement • How is it measured? • Using classical formulas published by Simrad and IFREMER • Adding our sonar specifics into them

  3. Methodology • Start with a set of formulas

  4. Methodology • Adapt to Sonar specific • e.g. bottom detection with blending

  5. Methodology • Use Monte-Carlo simulation to check the adaptation: • Simulate acoustic signal from bottom • Apply beamforming and bottom detection • Measure specific parameters: • Number of points used in phase processing • Blending coefficient

  6. Output of Monte Carlo simulation

  7. Adapting error model

  8. Published models • Simrad’s model • A priori model based on simple sonar characteristics • Can be tuned to matched observed performances • IFREMER’s model • Using in-depth sonar modeling • Accurate bottom detection characterization • Environment dependant • Increased level of complexity

  9. Comparison on a simple case • 30 meter depth, sandy bottom • 400kHz • 1.0 x 0.5 degree system • 220dB source level

  10. Comparison on a simple case • No baseline decorrelation • No shifting footprint

  11. Revisiting IFREMER’s model • Zero phase difference instant

  12. Phase bottom detection random error Linear regression Sample parameters from measured phase difference time series

  13. Depth error

  14. Increased model accuracy • Amplitude of signal varies according to Rayleigh law • New estimation of phase noise • Filtering of phase before regression • No exact derivation • Least Mean Square modeling

  15. Phase measurement error • When N >> 1 • With Rayleigh distribution modeling • With phase filtering

  16. Monte Carlo validation SNR at array output 20 dB, depth 25 m

  17. Experimental validations • SeaBat 7125 @ 400kHz • Three environments • Tank • Harbor (boat and sonar static) • In open water • Boat drifting • Sonar mounted over-the-side

  18. Tank test • Set the sonar on a rigid frame • Try to maximize coverage given confined space • Collect series of pings at high ping rate • Statistical analysis is not concluding • Need to get more realistic environment

  19. Harbor test • Set-up • Very shallow water • Increase incident angle range by tilting and rotating sonar

  20. Typical data

  21. Bottom topology • Average depth computed over 60 pings

  22. Uncertainty

  23. Real life versus models • Actual results better than prediction • Too small number of pings • IFREMER and RESON models match experimental data on phase detection • Too pessimistic on amplitude detection

  24. Effect of pulse length • Amplitude detection proportional to pulse length • Check validity of models

  25. Open sea test • Bottom topology (without refraction correction)

  26. Uncertainty results

  27. Conclusions • Better match between model and experimental data at larger depths • Still Shallow Water  • Some more tuning of model is required • Real-time output measures environmental conditions • Validation for other SeaBat will follow

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