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Maximum-likelihood and other processors for incoherent and coherent matched-field localization
Stan E Dosso1, Michael J Wilmut
1School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. sdosso@uvic.ca
The Journal of the Acoustical Society of America
|October 9, 2012
Summary
This study introduces optimal maximum-likelihood processors for underwater source localization, comparing their performance against existing methods using incomplete source knowledge. The research provides a framework for understanding and improving acoustic localization accuracy.
Area of Science:
- Underwater acoustics
- Signal processing
- Array signal processing
Background:
- Matched-field processing is crucial for underwater source localization.
- Incomplete knowledge of source characteristics (amplitude, phase) complicates localization.
- Existing methods for coherent processing with partial source information have limitations.
Purpose of the Study:
- To develop and compare maximum-likelihood (ML) processors for matched-field source localization.
- To analyze ML processors under various conditions of known/unknown source amplitude and phase variations.
- To establish a unifying framework for classifying and comparing different localization processors.
Main Methods:
- Development of ML processors by maximizing likelihood functions over unknown source spectral parameters.
- Analytical formulation to unify and compare different processor approaches.
- Numerical study using Monte Carlo simulations to quantify processor performance.
Main Results:
- ML processors are shown to be optimal estimators under Gaussian noise assumptions.
- A conceptual framework is provided for understanding processor performance based on source spectral information.
- Performance is quantified via probability of correct localization across various signal-to-noise ratios, frequencies, and sensor counts.
Conclusions:
- The developed ML processors offer improved accuracy for underwater source localization.
- The unifying framework aids in selecting appropriate processors based on available source information.
- Performance is sensitive to signal-to-noise ratio, number of frequencies, and sensor array size.
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