Year: 2026
Type: article
Abstract: This paper presents an integrated underwater Automatic Target Recognition (ATR) framework that synergizes advanced signal processing with deep learning. To eliminate non-stationary ambient ocean noise and isolate low-frequency modulations, the raw acoustic pressure signals undergo pre-denoising via Discrete Wavelet Transform (DWT with db4 mother wavelet and soft-thresholding), followed by Detection of Envelope Modulation on Noise (DEMON) analysis to yield 128-bin spectral feature vectors. Classification is performed using a dedicated 7-layer 1D Convolutional Neural Network (1D-CNN) architecture terminating in a Softmax output layer. Acoustic simulations in MATLAB R2024b generated a balanced dataset of 1,200 audio samples (200 samples per class across six distinct targets: fast boat, rubber zodiac, human diver, giant turtle, dolphin, and mini submarine) propagated through a 20–40 path shallow-water multipath channel ( ). Experimental evaluations demonstrate an overall classification accuracy of 94.93% and a macro-averaged F1-score of 0.9505. Coupled with an average inference latency of 11.40 ms per target and Euclidean distance-based thresholding for out-of-distribution anomaly detection, the proposed system proves computationally efficient and well-suited for real-time passive underwater surveillance.
Authors: النور , سعيدة, ابوستة , مرعي, Alnour , Saaida
Institution: Libyan Academy
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7214518231