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Real-time Uncertainty Output for MBES Systems
Eric Maillard, George Yufit, Pawel Pocwiardowski RESON, Inc
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Introduction What uncertainty? How is it measured?
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
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Methodology Start with a set of formulas
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Methodology Adapt to Sonar specific
e.g. bottom detection with blending
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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
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Output of Monte Carlo simulation
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Adapting error model
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Published models Simrad’s model IFREMER’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
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Comparison on a simple case
30 meter depth, sandy bottom 400kHz 1.0 x 0.5 degree system 220dB source level
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Comparison on a simple case
No baseline decorrelation No shifting footprint
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Revisiting IFREMER’s model
Zero phase difference instant
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Phase bottom detection random error
Linear regression Sample parameters from measured phase difference time series
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Depth error
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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
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Phase measurement error
When N >> 1 With Rayleigh distribution modeling With phase filtering
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Monte Carlo validation
Beam angle, degree. 45 52.2 60 Depth error, equation 2.58*10-4 2.94*10-4 3.39*10-4 Depth error, Monte - Carlo 3.23*10-4 2.87*10-4 4.41*10-4 SNR at array output 20 dB, depth 25 m
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Experimental validations
SeaBat 400kHz Three environments Tank Harbor (boat and sonar static) In open water Boat drifting Sonar mounted over-the-side
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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
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Harbor test Set-up Very shallow water
Increase incident angle range by tilting and rotating sonar
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Typical data
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Bottom topology Average depth computed over 60 pings
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Uncertainty
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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
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Effect of pulse length Amplitude detection proportional to pulse length Check validity of models
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Open sea test Bottom topology (without refraction correction)
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Uncertainty results
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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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