Design Considerations for Hybrid Underwater Target Tracking: Integrating Particle Filters and Deep Learning for Enhanced Decision

This paper delves into the key characteristics of Autonomous underwater vehicle (AUV) design, highlighting considerations such as hull structure, hydrodynamics, propulsion systems, and sensor integration. The main contribution of this paper is the tracking of underwater dynamic targets, distinguishing between reactive and deliberative tracking levels by proposing a novel hybrid tracking approach, the Multiple Model Particle Filter (MMPF), that integrates deep learning to predict dynamic target trajectory. Finally, a few simulations are delivered to demonstrate the efficiency of the proposed approach in tracking underwater maneuvering targets under different trajectory scenarios.