Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series

Signal Absorption-Based Range Estimator for Undersea Swarms

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2020.Robotic swarms are increasingly complex above the waterline due to reliable communication links. However, the limited propagation of similar signals in the ocean has impacted advances in undersea robotics. Underwater vehicles often rely on acoustics for navigation solutions; however, this presents challenges for robotic swarms. Many localization methods rely on precision time synchronization or two-way communication to estimate ranges. The cost of Chip-scale Atomic Clocks (CSACs) and acoustic modems is limiting for large-scale swarms due to the cost-per-vehicle and communications structure. We propose a single vehicle with reliable navigation as a "leader" for a scalable swarm of lower-cost vehicles that receive signals via a single hydrophone. This thesis outlines range estimation methods for sources with known signal content, including frequency and power at its origin. Transmission loss is calculated based on sound absorption in seawater and geometric spreading loss to estimate range through the Signal Absorption-Based Range Estimator (SABRE). SABRE's objective is to address techniques that support low-cost undersea swarming. This thesis's contributions include a novel method for range estimation onboard underwater vehicles that supports relative navigation through Doppler-shift methods for target bearing. This thesis develops the theory, algorithms, and analytical tools for real-world data range estimation