Hybrid Underwater Target Tracking with Particle Filters and Deep Learning; Focus on Design Considerations

This paper explores essential aspects of autonomous underwater vehicle (AUV) design, focusing on hull structure, hydrodynamics, propulsion systems, and sensor integration. It also examines the role of underwater Simultaneous Localization and Mapping (SLAM) in improving navigation and mapping precision for AUVs. In addition, the paper addresses challenges and solutions related to underwater GPS systems. The primary contribution of this study is a novel tracking method that combines the Particle Filter (PF) with deep learning techniques to enhance the tracking capabilities of underwater maneuvering targets. Multiple simulations validate the effectiveness of the proposed approach in diverse trajectory scenarios.