Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Non-linear echo cancellation - a Bayesian approach

Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel

Adapting Deep Learning for Underwater Acoustic Communication Channel Modeling

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work. Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and field experiments. The proposed test-bed also demonstrated the capabilities of developing and testing SDN protocols for a UWA network in both simulation and field experiments. Secondly, due to the lack of appropriate UWA CIR data sets for deep learning, a series of field UWA channel experiments have been conducted across a shallow freshwater river. Abundant UWA CIR data under various weather conditions have been collected and studied. The environmental factors that significantly affect the UWA channel state, including the solar radiation rate, the air temperature, the ice cover, the precipitation rate, etc., are analyzed in the case studies. The obtained UWA CIR data set with significant correlations to weather conditions can benefit future deep-learning research on UWA channels. Thirdly, a Wasserstein conditional generative adversarial network (WCGAN) is proposed to model the observed UWA CIR distribution. A power-weighted Jensen–Shannon divergence (JSD) is proposed to measure the similarity between the generated distribution and the experimental observations. The CIR samples generated by the WCGAN model show a lower power-weighted JSD than conventional estimated stochastic distributions. Finally, a modified conditional generative adversarial network (CGAN) model is proposed for predicting the UWA CIR distribution in the 15-minute range near future. This prediction model takes a sequence of historical and forecast weather information with a recent CIR observation as the conditional input. The generated CIR sample predictions also show a lower power-weighted JSD than conventional estimated stochastic distributions

Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

Environmental monitoring of coastal waters with a collaborative underwater acoustic and above water LoRaWAN sensor network

Climate changes are transforming the world as we know it and have a devastating impact on frail areas, such as coasts, afflicted by catastrophic events (rise in seawater temperature, floods) deteriorating the local biodiversity. Between the strategies undertaken to mitigate these effects, the EU Biodiversity Strategy for 2030 is one of the most ambitious. In particular, a relevant point is the inclusion of new solutions to monitor the conditions of the water, measuring specific parameters and polluting agents. However, up to today, there is no common ground when dealing with low-cost and low-power devices to collect data related to the quality of the water in coastal areas: a dense deployment of sensors would be the best option, but the technology used for long-range underwater acoustic communication is indeed extremely expensive. Nonetheless, in the last few years researchers have been investigating the possibilities given by low-cost and low-power acoustic modems, in the attempt to provide a way to employ dense deployment of underwater nodes. Another major turn in long-range low-power communications is the introduction of Low-Power Wide-Area Networks (LPWAN), which can be regarded as one of the most crucial entries in Internet of Things (IoT) applications. With this dissertation, we propose a network infrastructure for the tracking and the study of water quality parameters, to understand the impact they have on biodiversity. Specifically, we envision a system where there are two types of sensor nodes; one underwater and another on the water surface, forwarding the data they aggregate to one or more gateways. The gateways are connected to the Internet so that the data can be saved in a database for further processing. Underwater nodes use a part of the surface nodes as relays basing on an acoustic communication protocol, while the remaining surface nodes generate sensor data themselves; LoRa (together with LoRaWAN) has been chosen as the core LPWAN, enabling the long-range communication between the surface nodes and the gateways. Finally, the gateways are connected to the Internet with LTE standard. Simulations have been run to estimate the traffic requirements of the network as well as the feasibility of the system and a functioning prototype of a surface node has been developed. We selected a section of the Venice lagoon as reference area where our network could eventually be put in place, thus the simulations have been set according to this scenario. Also, the low-cost prototype has been tested and proved its full operativity

Best practice report – operation and maintenance requirements

Deliverable 3.6.3 from the MERiFIC Project A report prepared as part of the MERiFIC Project "Marine Energy in Far Peripheral and Island Communities"This report is a deliverable of MERiFIC Work Package 3.6: ‘Operation and Maintenance requirements’ and has been produced as a cross border collaboration between IFREMER and the University of Exeter. The report provides an overview of guidelines and recommendations for the management of O&M operations necessary for an optimal exploitation of Marine energy plants, with a focus on the specific areas of South West Cornwall, UK and Iroise sea, Brittany, France. An overview of the onshore infrastructures and ports possibly suitable for management of such O&M operations is also provided. Management of scheduled and unscheduled maintenance operations are discussed in their various aspects including site accessibility. It should be noted that this topic, including weather window assessment for operations is discussed in more details in the additional MERIFIC report D3.6.2: Best Practice for installation proceduresMERiFIC was selected under the European Cross-Border Cooperation Programme INTERREG IV A France (Channel) – England, co-funded by the ERDF