Abstract Traditional SONAR systems are widely used for underwater object detection and navigation; however, they suffer from high energy consumption, noise interference, and signal degradation in varying aquatic conditions. Inspired by biological echolocation, we propose a novel machine learning-enhanced sonar model to improve accuracy, robustness, and energy efficiency in underwater sensing applications. The proposed model employs bio-inspired echolocation principles, where transmitted acoustic pulses dynamically adjust in response to environmental conditions. Deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are employed to classify sonar echoes and enhance object detection. Additionally, digital signal processing (DSP) techniques, including Butterworth filtering, wavelet decomposition, and adaptive thresholding, are integrated to mitigate noise and improve signal clarity. Experimental evaluations demonstrate that the proposed sonar model, SonarNet, achieves 92.7% classification accuracy, outperforming conventional SONAR-based methods, which achieved 85.4% accuracy. The integration of adaptive signal processing leads to a 20.8% reduction in energy consumption compared to standard SONAR models, improves the Signal-to-Noise Ratio (SNR) by 15.3 dB, and reduces the false positive rate by 18.6%. Additionally, the sound velocity profile (SVP) correction mechanism improves depth estimation accuracy by 12.4%. All performance results reported in this work were obtained using a simulation environment parameterized by real-world oceanographic datasets.
Reddy et al. (Tue,) studied this question.