Розвиток методів визначення координат сенсорів у мобільній сенсорній мережі

Задачі дослідження: 1. Дослідити використання UWSN та представлену математичну модель на прикладі одиночного радіомаяка для отримання координат зануреного мобільного радіомаяка. 2. Проаналізувати критерії оцінки працездатності і застосовності алгоритмів визначення координат в сенсорних мережах. 3. Описати методику оперативного розрахунку координат проміжних точок маршруту руху літаючого інформаційного робота (ЛІР), який збирає інформацію з мобільних сенсорів мобільної бездротової сенсорної мережі (МБСМ). 4. Вирахувати якісні показники роботи запропонованого алгоритму визначення координат об’єктів в сенсорній мережі на базі технології ZigBee. 5. Розробка стартап проекту Мета роботи – аналіз методів визначення координат сенсорів в мобільних сенсорних мережах, а також дослідження визначення координат з використанням визначника Кейлі-Менгера.Research objectives: 1. Investigate the use of UWSN and the presented mathematical model on the example of a single radio beacon to obtain the coordinates of a submerged mobile radio beacon. 2. To analyze the criteria for evaluating the performance and applicability of algorithms for determining coordinates in sensor networks. 3. To describe the method of operative calculation of the coordinates of the intermediate points of the route of the flying information robot (FIR), which collects information from mobile sensors of the mobile wireless sensor network (MBSM). 4. Calculate the performance indicators of the proposed algorithm for determining the coordinates of objects in a sensor network based on ZigBee technology. 5. Development of a startup project The purpose of the work is to analyze the methods of determining the coordinates of sensors in mobile sensor networks, as well as to study the determination of coordinates using the Cayley-Menger determinant

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Towards localisation with Doppler radar

In this thesis the author introduces a novel method for Geo Localisation via Doppler Radar. The area of research is in the three dimensional space using amplitude and magnitude measurements. Geo Localisation in mobile applications is a useful technology that enables monitoring and gathering information about objects of interest

Fractal analyses of some natural systems

Fractal dimensions are estimated by the box-counting method for real world data sets and for mathematical models of three natural systems. 1 he natural systems are nearshore sea wave profiles, the topography of Shei-pa National Park in Taiwan, and the normalised difference vegetation index (NDV1) image of a fresh fern. I he mathematical models which represent the natural systems utilise multi-frequency sinusoids for the sea waves, a synthetic digital elevation model constructed by the mid-point displacement method for the topography and the Iterated Function System (IFS) codes for the fern leaf. The results show that similar fractal dimensions are obtained for discrete sub-sections of the real and synthetic one-dimensional wave data, whilst different fractal dimensions are obtained for discrete sections of the real and synthetic topographical and fern data. The similarities and differences are interpreted in the context of system evolution which was introduced by Mandelbrot (1977). Finally, the results for the fern images show that use of fractal dimensions can successfully separate void and filled elements of the two-dimensional series