The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency−domain multi−sub−band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal−to−noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets.

Seabed topography exerts a profound influence on underwater acoustic propagation, and the coupling effect between bottom acoustic properties and the source–receiver geometric configuration remains insufficiently quantified, particularly in seamount shielding scenarios. To address this gap, in this study, the BELLHOP ray model was integrated with Earth topography 1 (ETOPO1) topographic data and Hybrid Coordinate Ocean Model (HYCOM) hydrological data for seamounts east of Taiwan. Transmission loss (TL) of 300 Hz sound waves was simulated across four typical bottom types (rock, coarse sand, silt, and clay) under varying source depths (50–1000 m) and receiver depths (50–500 m). The maximum sonar detection range was delineated using an 80 dB TL threshold as the criterion for effective detection. The key findings reveal that the bottom properties are the primary factors that reduce the detection range: the maximum detection range over rock bottom exceeds that over clay by more than 8-fold. Notably, a shallow source–shallow receiver configuration mitigates the acoustic shadow effect induced by seamounts, whereas deep receiver deployment (≥500 m) diminishes the discriminative impact of bottom types on the propagation behavior. Furthermore, a segmented empirical prediction formula was established, which reconciles both the physical mechanisms (e.g., bottom reflection-absorption and seamount shielding) and engineering applicability. This formula provides a robust theoretical basis for evaluating sonar performance in complex seabed topography settings, thereby facilitating optimized underwater detection strategies in seamount-dominated marine environments.

A piezoelectric flextensional transducer was designed using the finite element method (FEM) for the passive detection of underwater frogmen. By studying the main performance parameters of sonar transducers for the detection of frogmen around the world, this paper presents the advantages of active and passive sonar in this task and proposes a passive method for the detection of frogmen. Subsequently, a Cymbal transducer was selected considering the needs of the research. After introducing the principles and methods of the FEM analysis, the acoustic and electromechanical characteristics of the Cymbal transducer were verified under the influence of structural dimensions, e.g., resonance frequency, conductance, and susceptance. The electromechanical performance of the Cymbal transducer was simulated with a FEM to study how it is affected by static water pressure and processing errors and to demonstrate the feasible application of the Cymbal transducer in practical applications. In the end, a Cymbal prototype was tested to verify that the designed Cymbal transducer performs in compliance with the requirements and can be used for the passive detection of underwater frogmen with open-circuit breathing.