Understanding and mitigating the impact of passing ships on underwater environmental estimation from ambient sounda)
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View abstract on PubMed
Summary
This summary is machine-generated.Low-rank interference degrades seabed type discrimination using ambient sound. A novel method reconstructs interference-free data, enabling accurate acoustic seabed analysis and improving bottom loss estimation.
Area Of Science
- Acoustic signal processing
- Geophysical remote sensing
- Statistical hypothesis testing
Background
- Distinguishing seabed types relies on analyzing acoustic data, often using covariance matrices.
- Box's M-test is a standard method for comparing seabed types based on covariance.
- Low-rank interference, like ship noise, can significantly impair the accuracy of these tests.
Purpose Of The Study
- To investigate the performance degradation of seabed type discrimination due to low-rank interference.
- To develop a method for mitigating the effects of such interference in acoustic data.
- To improve the accuracy of hypothesis testing for seabed characterization and bottom loss estimation.
Main Methods
- Formulated seabed type discrimination as a two-sample hypothesis test for covariance equality.
- Developed a method to construct a worst-case low-rank interference field.
- Implemented an alternating optimization procedure to recover the interference-free covariance matrix.
Main Results
- Constructed interference significantly degraded hypothesis testing performance in synthetic experiments.
- The proposed recovery method effectively eliminated interference for small interference ranks.
- Real-world experiments demonstrated successful interference mitigation and improved bottom loss estimation.
Conclusions
- Low-rank interference poses a significant challenge to acoustic seabed discrimination.
- The developed interference recovery technique enhances the reliability of hypothesis testing.
- This approach offers a practical solution for improving seabed characterization in noisy acoustic environments.
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