Nonlinear and Noise-Resilient Signal Processing for Enhanced Performance in Autonomous Systems and Underwater Acoustics

Abstract

Abstract. The increasing complexity of autonomous systems and underwater acoustics technologies necessitates the development of progressive signal dispensation methods that can handle nonlinear dynamics and mitigate the effects of noise. The problem lies in improving signal dispersion methods for autonomous systems and underwater acoustics to enhance accuracy, robustness, and overall system performance in complex environments. The objectives are to develop advanced nonlinear and noise-resilient signal processing techniques, enhance system performance, improve data accuracy, and ensure robust communication and navigation in autonomous systems and underwater acoustic environments. Adaptive bilateral kernel filtering (ABKF) enhances data preprocessing by effectively reducing noise, smoothing, and preserving edge details in nonlinear signal environments. Nonlinear iterative partial least squares (NIPALS) efficiently model complex relationships in data, improving signal extraction and reducing noise in autonomous systems and underwater acoustics. Reweighted sparse signal decomposition (RSSD) enhances noise resilience by effectively separating signals from noise, improving data quality in submerged audibility and autonomous systems. Hypergraph partitioning algorithm (HGPA) improves signal processing by efficiently partitioning complex data, enhancing performance, and reducing noise in autonomous systems and underwater acoustics. These systems can process real-time data with greater precision. The findings show that energy consumption varies with node count (6–20) across methods ABKF, NIPALS, RSSD, and HGPA. Energy starts near zero at six nodes, peaking around 350 units for ABKF, implemented in python software. Future scope focuses on real-time optimization, dynamic environment adaptability, and enhanced integration for autonomous navigation and underwater communication systems.