Submarine Navigation and Communication Model Based on HMM and TDA

The enigmatic depths of the ocean have captivated adventurers for centuries, offering vast, untapped resources that continue to spur exploration and exploitation. In response to the challenges of underwater navigation, this paper introduces an innovative submersible navigation and communication model that integrates the Hidden Markov Model (HMM) with Tactical Decision Aid (TDA), harnessing the principle of spiral motion. This model facilitates seamless collaboration between human decision-makers and remotely operated vehicles (ROVs), ensuring safer and more efficient underwater operations. The HMM simulates various potential routes and computes the most optimal path for navigation, allowing for more accurate movement predictions. During the mission, the TDA is continuously updated with real-time acoustic data, enhancing both navigation precision and communication between the submersible and surface operators. By combining machine learning with TDA, the model provides decision-makers with precise guidance while maintaining confidence in the ROV's embedded intelligence. This integrated approach ensures reliable underwater operations, offering a promising solution for navigating the complexities of deep-sea exploration.