Esta dissertação desenvolve filtro de informação, algoritmos array para estimador do erro médio mínimo quadrático para sistemas lineares sujeitos a saltos Markovianos e algoritmos array rápidos para filtragem de sistemas singulares convencionais. Exemplos numéricos serão apresentados para mostrarem as vantagens dos algoritmos array deduzidos. Parte dos resultados obtidos nesta pesquisa serão publicados no seguinte artigo: Terra et al. (2007). Terra, M. H., Ishihara, J. Y. and Jesus, G. Q. (2007). Information filtering and array algorithms for discrete-time Markovian jump linear systems. Proceedings of the American Control Conference ACC07. / This dissertation develops information filter and array algorithms for linear minimum mean square error estimator (LMMSE) of discrete-time Markovian jump linear systems (MJLSs) and fast array algorithms for filtering of standard singular systems. Numerical examples to show the advantage of the array algorithms are presented. Some results obtained in this research are published in the following paper: Terra et al. (2007). Terra, M. H., Ishihara, J. Y. and Jesus, G. Q. (2007). Information filtering and array algorithms for discrete-time Markovian jump linear systems. Proceedings of the American Control Conference ACC07.

This dissertation lays out a multi-static radar system with mobile receivers. The transmitter is at a known location emitting a radar signal that bounces off a target. The echo is received by a team of UAVs that are capable of estimating both time-delay and Doppler from the received signal. Several methods for controlling the movement of mobile sensor platforms are presented to improve target tracking performance. Two optimization criteria are derived for the problem, both of which require some type of search procedure to find the desired solution. Simulations are used to show the benefit of using closed-loop sensor control for the special case of an EKF tracking filter. In addition, a simpler closed-form approach based on one of the algorithms is also presented and is shown to have performance similar to that obtained using the optimal algorithms. To decentralize the estimation in the UAV network, an information consensus filter (ICF) is presented. In the ICF each agent maintains a local estimate, which is shown to be unbiased and conservative with respect to the local covariance matrix estimate. The ICF does not take into account unknown track-to-track correlation that occurs when local independent estimates pass through a common process model. However, it does eliminate the redundancy incurred when communicating information through general network topologies, including graphs containing loops. In the ICF a discrete-time consensus filter is used to handle the communication of information between nodes (UAVs) in the network. Communication is local in that each agent can only communicate with local neighbors and not the entire network. A second-order discrete-time consensus protocol is developed. Necessary and sufficient conditions are given that ensure the team of agents achieves consensus using the second-order protocol. Using insights from the analysis of the ICF an extension is made by adding an observation buffer to the ICF. The new filter is called the information consensus filter with an observation buffer (ICFOB). The track-to-track correlation occurring from independent estimates passing through a common process model does not affect the ICFOB as it does other decentralized estimation methods. The ICFOB is shown to be equivalent to a centralized filter that has access to every measurement in a network. There are two caveats to this equivalency. First, at any point in time, the prior ICFOB estimate is equal to the prior centralized filter estimate found by fusing the observations that are taken before those stored in the buffer. The a posteriori estimates using observations in the buffer are not equal to estimates from the centralized filter since the agents have not finished disseminating those observations throughout the sensor network. Second, the ICFOB needs to know the number of active sensors in the network. The number of sensors is global information; therefore, the ICFOB is not fully decentralized. If the number of sensors is not known, the local estimates are conservative.

Este trabalho trata de filtragem robusta para sistemas lineares sujeitos a saltos Markovianos discretos no tempo. Serão desenvolvidas estimativas preditoras e filtradas baseadas em algoritmos recursivos que são úteis para aplicações em tempo real. Serão desenvolvidas duas classes de filtros robustos, uma baseada em uma estratégia do tipo H \'INFINITO\' e a outra baseada no método dos mínimos quadrados regularizados robustos. Além disso, serão desenvolvidos filtros na forma de informação e seus respectivos algoritmos array para estimar esse tipo de sistema. Neste trabalho assume-se que os parâmetros de saltos do sistema Markoviano não são acessíveis. / This work deals with the problem of robust state estimation for discrete-time uncertain linear systems subject to Markovian jumps. Predicted and filtered estimates are developed based on recursive algorithms which are useful in on-line applications. We develop two classes of filters, the first one is based on a H \'INFINITO\' approach and the second one is based on a robust regularized leastsquare method. Moreover, we develop information filter and their respective array algorithms to estimate this kind of system. We assume that the jump parameters of the Markovian system are not acessible.

Este trabalho trata de filtragem robusta para sistemas lineares sujeitos a saltos Markovianos discretos no tempo. Serão desenvolvidas estimativas preditoras e filtradas baseadas em algoritmos recursivos que são úteis para aplicações em tempo real. Serão desenvolvidas duas classes de filtros robustos, uma baseada em uma estratégia do tipo H \'INFINITO\' e a outra baseada no método dos mínimos quadrados regularizados robustos. Além disso, serão desenvolvidos filtros na forma de informação e seus respectivos algoritmos array para estimar esse tipo de sistema. Neste trabalho assume-se que os parâmetros de saltos do sistema Markoviano não são acessíveis. / This work deals with the problem of robust state estimation for discrete-time uncertain linear systems subject to Markovian jumps. Predicted and filtered estimates are developed based on recursive algorithms which are useful in on-line applications. We develop two classes of filters, the first one is based on a H \'INFINITO\' approach and the second one is based on a robust regularized leastsquare method. Moreover, we develop information filter and their respective array algorithms to estimate this kind of system. We assume that the jump parameters of the Markovian system are not acessible.

在資訊爆炸的時代，處於日趨複雜的環境及多重資訊來源管道之下，如何從大量及瑣碎的資訊中找出「重要且有用」的部份，藉以輔助企業或個人制定正確的決策，並降低資訊取得的成本，是資訊人員在設計資訊系統時所必須考量的重要因素之一，因此，資訊篩選(Information filtering)已成為當務之急，更顯示出其重要性。 本研究之主要目的在於整合類神經網路與模糊理論以建立一個通用型資訊篩選之演算法，藉由此演算法可篩選出重要之決策變數，減少資訊的使用量，達到相同或類似的決策結果，進而降低後續資訊蒐集之成本。最後並以四個XOR實驗及國內上市公訂股價預測為例，以測試本研究所開發出來之演算法的正確性及實用性。就XOR實驗結果顯示均能迅速且正確的篩選出重要的輸入資訊；而在股價預測方面，結合基本面分析及技術面分析，根據個別公司的特性及不同的時間點，能夠篩選出其重要的預測變數，可作為股市投資者之重要參考依據。因此，藉由本演算法所開發出來的系統，可以達到資訊篩選的目的。 / At the time of information explosion, how to filter the important and useful parts from a large and trivial information pool is one of the most important factors considering in designing information systems which are used to assist users making right decisions by MIS managers. The purpose of this research is to integrate two technologies. Artificial Neural Network and Fuzzy Theory, to develop a generalized algorithm to filter important information. We hope that using this algorithm we can (1)filter the important decision variables, (2)decrease the information usage, and (3)reduce the cost of information collection. Finally, we made four experiments on the XOR system and stock market forecasting to test the accuracy and practicability of the information filter algorithm. The results of experiments showed that the algorithm could filter the important information correctly and quickly.