[pt] Este trabalho apresenta uma descrição teórica e a implementação de um método analítico para o cálculo das estatísticas das interferências produzidas por redes não- GSO em elementos de outras redes não-GSO, de redes GSO ou do Serviço Fixo Terrestre. O método é baseado na função densidade de probabilidade da posição de um satélite (satélite de referência) da rede não-GSO, a partir da qual é possível determinar a função densidade de probabilidade da interferência produzida por esta rede no elemento interferido. O método foi anteriormente desenvolvido para a análise de interferências envolvendo em satélites de trajetória não-repetitiva [2]. O objetivo deste trabalho é estender a aplicação do método para o caso de satélites de trajetória repetitiva, através de um procedimento alternativo onde a posição do satélite de referência é representada pela sua anomalia media e pela longitude do nó de ascensão. Verificou-se que este novo procedimento, desenvolvido para aplicação ao caso de satélites de trajetória repetitiva, é também aplicável ao caso de redes que utilizam satélites de trajetória não-repetitiva. Vários exemplos são apresentados para ilustrar a aplicação do procedimento desenvolvido a situações de interesse prático. / [en] This work presents the theoretical description and implementation of an analytical method for the evaluation of the statistical behavior of the interference produced or received by elements of a non-GSO network. This method is based on the knowledge of the probability density function of the position of one of the satellites of the non-GSO constellation (reference satellite). This probability density function can be used to determine the probability density function of the interference produced (or received) by this network. This method was previously developed for the interference analysis that involve non-repeated track satellites [2]. This work has extended the method to the case of repeated track satellites by using an alternative procedure where the reference satellite position is expressed in terms of its mean anomaly and the longitude of its ascending node. It was verified that this new procedure, developed for the case of repeated track satellites is also applicable to the case of non-repeated track satellites networks. Several examples are presented to illustrate the application of the developed procedure in situations of practical interest.

[pt] Neste trabalho é desenvolvido um modelo matemático para descrever o comportamento estatístico da interferência produzida por redes VSAT/MFTDMA. O modelo proposto é utilizado para avaliar a interferência produzida pelos lances de subida de enlaces VSAT/MF-TDMA em enlaces de uma outra rede que utiliza um satélite vizinho. No modelo proposto, expressões analíticas foram desenvolvidas para levar em conta os efeitos de variações nas potências transmitidas, nos tamanhos das antenas e nos erros de apontamento das antenas transmissoras. As posições geográficas das estações terrenas são modeladas por processos pontuais de Poisson, bi-dimensionais. O modelo proposto é suficientemente geral para acomodar outros tipos de processos pontuais, além de situações envolvendo áreas de serviço contendo múltiplos tipos de distribuição geográfica das estações terrenas. Resultados numéricos obtidos com o modelo proposto são comparados àqueles baseados em valores reais de parâmetros (e.g. localização das estações terrenas, tamanhos de antenas e potências de transmissão) que foram fornecidos por um operador brasileiro de satélites. / [en] In this work a mathematical model to describe the statistical behavior of the interference produced by VSAT/MF-TDMA networks is developed. The model is used to assess the interference produced by the uplinks of a VSAT/MF-TDMA network into links of a network that uses a neighboring satellite. In the proposed model, analytical expressions were developed to account for the effects of the varying transmitting powers, antenna sizes, and transmitting antenna pointing errors. The earth station locations are modeled by a two dimensional Poisson point process. The model is general enough to accommodate other types of point processes and can be applied to situations involving service areas containing multiple types of earth station geographical distribution. Numerical results obtained with the proposed model are compared to those based on the actual parameters values (e.g. earth station locations, antenna sizes and transmitting powers) which were provided by a Brazilian satellite operator.

Multimodal function optimization generally focuses on algorithms to find either a local optimum or the global optimum while avoiding local optima. However, there is another class of optimization problems which have the objective of finding multiple optima with either equal or unequal function values. The knowledge of multiple local and global optima has several advantages such as obtaining an insight into the function landscape and selecting an alternative solution when dynamic nature of constraints in the search space makes a previous optimum solution infeasible to implement. Applications include identification of multiple signal sources like sound, heat, light and leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear/ chemical spills, fire-origins in forest fires and hazardous chemical discharge in water bodies, oil spills, deep-sea hydrothermal vent plumes, etc. Signals such as sound, light, and other electromagnetic radiations propagate in the form of a wave. Therefore, the nominal source profile that spreads in the environment can be represented as a multimodal function and hence, the problem of localizing their respective origins can be modeled as optimization of multimodal functions. Multimodality in a search and optimization problem gives rise to several attractors and thereby presents a challenge to any optimization algorithm in terms of finding global optimum solutions. However, the problem is compounded when multiple (global and local) optima are sought. This thesis develops a novel glowworm swarm optimization (GSO) algorithm for simultaneous capture of multiple optima of multimodal functions. The algorithm shares some features with the ant-colony optimization (ACO) and particle swarm optimization (PSO) algorithms, but with several significant differences. The agents in the GSO algorithm are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the function-profile values at their current locations into a luciferin value and broadcast the same to other agents in their neighborhood. The glowworm depends on a variable local decision domain, which is bounded above by a circular sensor range, to identify its neighbors and compute its movements. Each glowworm selects a neighbor that has a luciferin value more than its own, using a probabilistic mechanism, and moves toward it. That is, they are attracted to neighbors that glow brighter. These movements that are based only on local information enable the swarm of glowworms to partition into disjoint subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple optima (not necessarily equal) of a given multimodal function. Natural glowworms primarily use the bioluminescent light to signal other individuals of the same species for reproduction and to attract prey. The general idea in the GSO algorithm is similar in these aspects in the sense that glowworm agents are assumed to be attracted to move toward other glowworm agents that have brighter luminescence (higher luciferin value). We present the development of the GSO algorithm in terms of its working principle, various algorithmic phases, and evolution of the algorithm from the first version of the algorithm to its present form. Two major phases ¡ splitting of the agent swarm into disjoint subgroups and local convergence of agents in each subgroup to peak locations ¡ are identified at the group level of the algorithm and theoretical performance results related to the latter phase are obtained for a simplified GSO model. Performance of the GSO algorithm against a large class of benchmark multimodal functions is demonstrated through simulation experiments. We categorize the various constants of the algorithm into algorithmic constants and parameters. We show in simulations that fixed values of the algorithmic constants work well for a large class of problems and only two parameters have some influence on algorithmic performance. We also study the performance of the algorithm in the presence of noise. Simulations show that the algorithm exhibits good performance in the presence of fairly high noise levels. We observe graceful degradation only with significant increase in levels of measurement noise. A comparison with the gradient based algorithm reveals the superiority of the GSO algorithm in coping with uncertainty. We conduct embodied robot simulations, by using a multi-robot-simulator called Player/Stage that provides realistic sensor and actuator models, in order to assess the GSO algorithm's suitability for multiple source localization tasks. Next, we extend this work to collective robotics experiments. For this purpose, we use a set of four wheeled robots that are endowed with the capabilities required to implement the various behavioral primitives of the GSO algorithm. We present an experiment where two robots use the GSO algorithm to localize a light source. We discuss an application of GSO to ubiquitous computing based environments. In particular, we propose a hazard-sensing environment using a heterogeneous swarm that consists of stationary agents and mobile agents. The agents deployed in the environment implement a modification of the GSO algorithm. In a graph of mini mum number of mobile agents required for 100% source-capture as a function of the number of stationary agents, we show that deployment of the stationary agents in a grid configuration leads to multiple phase-transitions in the heterogeneous swarm behavior. Finally, we use the GSO algorithm to address the problem of pursuit of multiple mobile signal sources. For the case where the positions of the pursuers and the moving source are collinear, we present a theoretical result that provides an upper bound on the relative speed of the mobile source below which the agents succeed in pursuing the source. We use several simulation scenarios to demonstrate the ecacy of the algorithm in pursuing mobile signal sources. In the case where the positions of the pursuers and the moving source are non-collinear, we use numerical experiments to determine an upper bound on the relative speed of the mobile source below which the pursuers succeed in pursuing the source.

Locating an odor source in a turbulent environment, an instinctive behavior of insects such as moths, is a nontrivial task in robotics. Robots equipped with odor sensors find it difficult to locate the odor source due to the sporadic nature of odor patches in a turbulent environment. In this thesis, we develop a swarm algorithm which acquires information from odor patches and utilizes it to locate the odor source. The algorithm utilizes an intelligent integration of the chemotaxis, anemotaxis and spiralling approaches, where the chemotactic behavior is implemented by the recently proposed Glowworm Swarm Optimization (GSO) algorithm. Agents switch between chemotactic, anemotactic, and spiralling modes in accordance with the information available from the environment for optimal performance. The proposed algorithm takes full advantage of communication and collaboration between the robots. It is shown to be robust, efficient and well suited for implementation in olfactory robots. An important feature of the algorithm is the use of maximum concentration encountered in the recent past for navigation, which is seen to improve algorithmic performance significantly. The algorithm initially assumes agents to be point masses, later this is modified for robots and includes a gyroscopic avoidance strategy. A variant of the algorithm which does not demand wind information, is shown to be capable of locating odor sources even in no wind environment. A deterministic GSO algorithm has been proposed which is shown capable of faster convergence. Another proposed variant, the push pull GSO algorithm is shown to be more efficient in the presence of obstacle avoidance. The proposed algorithm is also seen capable of locating odor source under varying wind conditions. We have also shown the simultaneous capture of multiple odor sources by the proposed algorithm. A mobile odor source is shown to be captured and tracked by the proposed approach. The proposed approaches are later tested on data obtained from a realistic dye mixing experiment. A gas source localization experiment is also carried out in the lab to demonstrate the validity of the proposed approaches under real world conditions.