An accurate RSS/AoA-based localization method for internet of underwater things

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors

Acoustic underwater target tracking methods using autonomous vehicles

Marine ecological research related to the increasing importance which the fisheries sector has reached so far, new methods and tools to study the biological components of our oceans are needed. The capacity to measure different population and environmental parameters of marine species allows a greater knowledge of the human impact, improving exploitation strategies of these resources. For example, the displacement capacity and mobility patterns are crucial to obtain the required knowledge for a sustainable management of fisheries. However, underwater localisation is one of the main problems which must be addressed in subsea exploration, where no Global Positioning System (GPS) is available. In addition to the traditional underwater localisation systems, such as Long BaseLine (LBL) or Ultra-Short BaseLine (USBL), new methods have been developed to increase navigation performance, flexibility, and to reduce deployment costs. For example, the Range-Only and Single-Beacon (ROSB) is based on an autonomous vehicle which localises and tracks different underwater targets using slant range measurements conducted by acoustic modems. In a moving target tracking scenario, the ROSB target tracking method can be seen as a Hidden Markov Model (HMM) problem. Using Bayes' rule, the probability distribution function of the HMM states can be solved by using different filtering methods. Accordingly, this thesis presents different strategies to improve the ROSB localisation and tracking methods for static and moving targets. Determining the optimal parameters to minimize acoustic energy use and search time, and to maximize the localisation accuracy and precision, is therefore one of the discussed aspects of ROSB. Thus, we present and compare different methods under different scenarios, both evaluated in simulations and field tests. The main mathematical notation and performance of each algorithm are presented, where the best practice has been derived. From a methodology point of view, this work advances the understanding of accuracy that can be achieved by using ROSB target tracking methods with autonomous vehicles. Moreover, whereas most of the work conducted during the last years has been focused on target tracking using acoustic modems, here we also present a novel method called the Area-Only Target Tracking (AOTT). This method works with commercially available acoustic tags, thereby reducing the costs and complexity over other tracking systems. These tags do not have bidirectional communication capabilities, and therefore, the ROSB techniques are not applicable. However, this method can be used to track small targets such as jellyfish due to the reduced tag's size. The methodology behind the area-only technique is shown, and results from the first field tests conducted in Monterey Bay area, California, are also presented.La biologia marina junt amb la importància que ha adquirit el sector pesquer, fa que es requereixin noves eines per a l’estudi dels nostres oceans. La capacitat de mesurar diferents poblacions i paràmetres ambientals d’espècies marines permet millorar el coneixement de l’impacte que l’ésser humà té sobre elles, millorant-ne els mètodes d’explotació. Per exemple, la capacitat de desplaçament i els patrons de moviment són crucials per obtenir el coneixement necessari per a una explotació sostenible de les pescaries involucrades. No obstant, la localització submarina és un dels principals problemes que s’ha de resoldre en l’explotació dels recursos submarins, on el sistema de posició global (GPS) no es pot utilitzar. A part dels mètodes tradicionals de posicionament submarí, com per exemple el Long Base-Line (LBL) o el Ultra-Short Base-Line (USBL), nous mètodes han estat desenvolupats per tal de millorar la navegació, la flexibilitat, i per reduir els costos de desplegament. Per exemple, el Range-Only and Single-Beacon (ROSB) utilitza un vehicle autònom per a localitzar i seguir diferents objectius submarins mitjançant mesures de rang realitzades a partir de mòdems acústics. En un escenari on l’objectiu a seguir és mòbil, el mètode ROSB de seguiment pot ser vist com a un problema de Hidden Markov Model (HMM). Aleshores, utilitzant la regla de Bayes, la funció de distribució de probabilitat dels estats del HMM pot ser solucionat utilitzant diferents mètodes de filtratge. Per tant, s’estudien diferents estratègies per millorar el sistema de localització i seguiment basat en ROSB, tant per objectius estàtics com mòbils. En aquesta tesis, presentem i comparem diferents mètodes utilitzant diferents escenaris, els quals s’han avaluat tant en simulacions com en proves de camp reals. A més, es presenten les principals notacions matemàtiques de cada algoritme i les millors pràctiques a utilitzar. Per tant, des d’un punt de vista metodològic, aquest treball fa un pas endavant en el coneixement de l’exactitud que es pot assolir utilitzant els mètodes de localització i seguiment d’espècies mitjançant algoritmes ROSB i vehicles autònoms. A més a més, mentre molts dels treballs realitzant durant els últims anys es centren en l’ús de mòdems acústics per al seguiment d’objectius submarins, en aquesta tesis es presenta un innovador mètode anomenat Area-Only Target Tracking (AOTT). Aquest sistema utilitza petites etiquetes acústiques comercials (tag), la qual cosa, redueix el cost i la complexitat en comparació amb els altres mètodes. Addicionalment, gràcies a l’ús d’aquests tags de dimensions reduïdes, aquest sistema permet seguir espècies marines com les meduses. La metodologia utilitzada per el mètode AOTT es mostra en aquesta tesis, on també es presenten els primers experiments realitzats a la badia de Monterey a Califòrnia