Spread Spectrum Modulation with Grassmannian Constellations for Mobile Multiple Access Underwater Acoustic Channels

The objective of this study is to evaluate Grassmannian constellations combined with a spread spectrum multiple access scheme for underwater acoustic mobile multiple access communication systems. These communication systems enable the coordination of a fleet of Autonomous Underwater Vehicles (AUVs) from a surface or bottom control unit, e.g., a boat. Due to its robustness against phase rotation, the demodulator of Grassmannian constellations uses non-coherent detection, and the main advantage of such modulation lies in the spectrum efficiency gain with respect to conventional differential modulation. The communication system under study in this paper consists of (i), at the transmitter side, a Grassmannian modulation used in an orthogonal spread spectrum multiple access scheme called Multiuser Hyperbolic Frequency Modulation (MU-HFM) and (ii), at the receiver side, a non-coherent array decoder. The modulation and demodulation are presented as well as the considered spreading sequences. Finally, performances of the proposed transmission scheme are evaluated over replayed underwater acoustic channel responses collected at sea by a multi-sensor acoustic acquisition system.Spread Spectrum Modulation with Grassmannian Constellations for Mobile Multiple Access Underwater Acoustic ChannelspublishedVersio

Adaptive Modulation Schemes for Underwater Acoustic OFDM Communication

High data rate communication is challenging in underwater acoustic (UA) communication as UA channels vary fast along with the environmental factors. A real-time Orthogonal frequency-division multiplexing (OFDM) based adaptive UA communication system is studied in this research employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. The developed adaptive modulation schemes enhance the reliability of communication, guarantee continuous connectivity, ensure maximum performance under a fixed BER at all times and boost data rate

Underwater Localization in a Confined Space Using Acoustic Positioning and Machine Learning

Localization is a critical step in any navigation system. Through localization, the vehicle can estimate its position in the surrounding environment and plan how to reach its goal without any collision. This thesis focuses on underwater source localization, using sound signals for position estimation. We propose a novel underwater localization method based on machine learning techniques in which source position is directly estimated from collected acoustic data. The position of the sound source is estimated by training Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Convolutional Neural Network (CNN). To train these data-driven methods, data are collected inside a confined test tank with dimensions of 6m x 4.5m x 1.7m. The transmission unit, which includes Xilinx LX45 FPGA and transducer, generates acoustic signal. The receiver unit collects and prepares propagated sound signals and transmit them to a computer. It consists of 4 hydrophones, Red Pitay analog front-end board, and NI 9234 data acquisition board. We used MATLAB 2018 to extract pitch, Mel-Frequency Cepstrum Coefficients (MFCC), and spectrogram from the sound signals. These features are used by MATLAB Toolboxes to train RF, SVM, FNN, and CNN. Experimental results show that CNN archives 4% of Mean Absolute Percentage Error (MAPE) in the test tank. The finding of this research can pave the way for Autonomous Underwater Vehicle (AUV) and Remotely Operated Vehicle (ROV) navigation in underwater open spaces

Acoustic system for ground truth underwater positioning in DEEC's test tank

Desenvolvimento de um sistema acústico de posicionamento capaz de estimar, em tempo real, a posição tridimensional de objetos dentro do tanque de ensaios do DEEC.A obtenção desta posição "ground truth" é fundamental para o apoio a ensaios de sistemas de navegação subaquáticos e para o controlo de veículos robóticos tais como AUV's e ROV's.Development of an acoustic positioning system, capable of estimating, in real time, the three-dimensional position of an object inside the DEEC's test tank. The ability to obtain this ground truth position is fundamental to support tests of underwater navigation systems, and to the control of robotic vehicles such as AUV's and ROV's

Automated tracking of the Florida manatee (Trichechus manatus)

The electronic, physical, biological and environmental factors involved in the automated remote tracking of the Florida manatee (Trichechus manatus) are identified. The current status of the manatee as an endangered species is provided. Brief descriptions of existing tracking and position locating systems are presented to identify the state of the art in these fields. An analysis of energy media is conducted to identify those with the highest probability of success for this application. Logistic questions such as the means of attachment and position of any equipment to be placed on the manatee are also investigated. Power sources and manateeborne electronics encapsulation techniques are studied and the results of a compter generated DF network analysis are summarized

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