Bayesian Optimization-Tuned machine learning for underwater acoustic target localization

Underwater acoustic target localization (UATL) is challenging but has achieved some success with limitations. For example, matched field processing (MFP) is sensitive to environmental noise and inefficient in processing large-scale data, making real-time with accurate performance difficult. This paper presents a Bayesian optimization-tuned machine learning approach for UATL and conducts comparative studies with MFP and other parameter tuning methods. The environment used is from the seatrial conducted on October 26, 1993, in the shallow sea area north of Elba Island. First, the simulated training data is generated by the KRAKEN propagation code on grids of ranges and depths. Second, MFP and two machine learning methods (k-nearest neighbor, support vector regression) with distinct hyperparameter optimization approaches are employed for localization. The results show that the machine learning approaches achieve higher localization accuracy than MFP, identifying the underwater target located at a 5.6 km range (error < 0.1 km) and 79 m depth (error < 0.5 m), while Bayesian optimization proves more efficient than alternative tuning methods.