Rhythm-aware adaptive spectro-temporal enhancement for underwater acoustic target recognition.
Passive acoustic target recognition is often constrained by the complex interplay of variable underwater propagation channels and nonstationary target states. While data-driven deep learning models offer exceptional flexibility in feature learning, they are frequently susceptible to overfitting environmental noise and site-specific cues, which undermines their generalization in fluctuating marine conditions. Conversely, methods grounded in physical attributes exhibit superior intrinsic stability across diverse environments but typically lack the comprehensive signal perception and discriminative richness required for sophisticated classification. To bridge this gap, we propose rhythm-aware adaptive spectro-temporal enhancement (RASTE), prioritizing physical interpretability and robustness. Unlike traditional detection of envelope modulation on noise methods that require manual bandpass filter selection and assume signal stationarity, RASTE adaptively extracts rhythmic signatures and maintains efficacy, even under non-stationary conditions, such as pulsed interference. These features are applied as a soft mask to spectrograms to integrate physical priors while preserving the integrity of discriminative features. Experiments on open-source datasets indicate that RASTE serves as a robust and interpretable alternative to baselines. By navigating the performance-interpretability trade-off, RASTE achieves competitive results, particularly in scenarios characterized by pronounced rhythmic structures.