Passive localization of dual targets in deep-ocean direct-arrival zone using a horizontal line array.

Area of Science:

• Ocean acoustics
• Signal processing
• Array signal processing

Background:

• Passive localization of dual targets (surface ship, submerged source) is challenging.
• Similar source levels and deep-ocean conditions complicate localization.
• Traditional methods like minimum variance distortionless response have limitations.

Purpose of the Study:

• Develop a passive localization method for dual targets using multipath arrival angles.
• Improve resolution for multipath arrival angles under coherent signal conditions.
• Enhance effective range and localization accuracy.

Main Methods:

• Utilized a horizontal line array for passive localization.
• Employed sparse Bayesian learning (SBL) for improved multipath angle resolution.
• Compared SBL with the classical minimum variance distortionless response (MVDR) method.

Main Results:

• Sparse Bayesian learning significantly improved multipath arrival angle resolution.
• The proposed method demonstrated enhanced effective range and localization accuracy.
• Method effectiveness validated through simulations and experimental data.

Conclusions:

• The developed passive localization method based on multipath arrival angles is effective for dual targets.
• Sparse Bayesian learning offers advantages over traditional methods for coherent signals.
• The approach shows promise for practical applications in underwater acoustics.