Apresenta-se o desenvolvimento de modelos matemáticos e algoritmos de fusão sensorial para navegação terrestre usando uma unidade de medição inercial (UMI) de baixo desempenho e o Filtro Estendido de Kalman. Os modelos foram desenvolvidos com base nos sistemas de navegação inercial strapdown (SNIS). O termo baixo desempenho refere-se à UMIs que por si só não são capazes de efetuar o auto- alinhamento por girocompassing. A incapacidade de se navegar utilizando apenas uma UMI de baixo desempenho motiva a investigação de técnicas que permitam aumentar o grau de precisão do SNIS com a utilização de sensores adicionais. Esta tese descreve o desenvolvimento do modelo completo de uma fusão sensorial para a navegação inercial de um veículo terrestre usando uma UMI de baixo desempenho, um hodômetro e uma bússola eletrônica. Marcas topográficas (landmarks) foram instaladas ao longo da trajetória de teste para se medir o erro da estimativa de posição nesses pontos. Apresenta-se o desenvolvimento do Filtro de Kalman Adaptativo Suavizado (FKAS), que estima conjuntamente os estados e o erro dos estados estimados do sistema de fusão sensorial. Descreve-se um critério quantitativo que emprega as incertezas de posição estimadas pelo FKAS para se determinar a priori, dado os sensores disponíveis, o intervalo de tempo máximo que se pode navegar dentro de uma margem de confiabilidade desejada. Conjuntos reduzidos de landmarks são utilizados como sensores fictícios para testar o critério de confiabilidade proposto. Destacam-se ainda os modelos matemáticos aplicados à navegação terrestre, unificados neste trabalho. Os resultados obtidos mostram que, contando somente com os sensores inerciais de baixo desempenho, a navegação terrestre torna-se inviável após algumas dezenas de segundos. Usando os mesmos sensores inerciais, a fusão sensorial produziu resultados muito superiores, permitindo reconstruir trajetórias com deslocamentos da ordem de 2,7 km (ou 15 minutos) com erro final de estimativa de posição da ordem de 3 m. / This work presents the development of the mathematical models and the algorithms of a sensor fusion system for terrestrial navigation using a low-grade inertial measurement unit (IMU) and the Extended Kalman Filter. The models were developed on the basis of the strapdown inertial navigation systems (SINS). Low-grade designates an IMU that is not able to perform girocompassing self-alignment. The impossibility of navigating relying on a low performance IMU is the motivation for investigating techniques to improve the SINS accuracy with the use of additional sensors. This thesis describes the development of a comprehensive model of a sensor fusion for the inertial navigation of a ground vehicle using a low-grade IMU, an odometer and an electronic compass. Landmarks were placed along the test trajectory in order to allow the measurement of the error of the position estimation at these points. It is presented the development of the Smoothed Adaptive Kalman Filter (SAKF), which jointly estimates the states and the errors of the estimated states of the sensor fusion system. It is presented a quantitative criteria which employs the position uncertainties estimated by SAKF in order to determine - given the available sensors, the maximum time interval that one can navigate within a desired reliability. Reduced sets of landmarks are used as fictitious sensors to test the proposed reliability criterion. Also noteworthy are the mathematical models applied to terrestrial navigation that were unified in this work. The results show that, only relying on the low performance inertial sensors, the terrestrial navigation becomes impracticable after few tens of seconds. Using the same inertial sensors, the sensor fusion produced far better results, allowing the reconstruction of trajectories with displacements of about 2.7 km (or 15 minutes) with a final error of position estimation of about 3 m.

Com o aumento do uso de sistemas elétricos, eletrônicos e programáveis em aplicações de diversos domínios, tais como entretenimento, realização de transações financeiras, distribuição de energia elétrica, controle de processos industriais e sinalização e controle em transporte de passageiros e carga, é essencial que as políticas de manutenção utilizadas sejam capazes de minimizar os custos associados a eventuais falhas que afetem negativamente os serviços providos. Ao longo das últimas décadas, foi sedimentada a tendência de que a adoção de técnicas de manutenção preditiva representa uma das abordagens mais viáveis e promissoras para que falhas de sistemas utilizados em diversas aplicações possam ser detectadas antes de elas efetivamente ocorrerem. Considerando-se que uma parcela significativa dos estudos recentes na área de manutenção preditiva de sistemas apresenta como limitação o custo elevado para se instalar uma infraestrutura específica para realizar a coleta de dados que serão usados para dar suporte à predição das falhas futuras de um sistema, o modelo proposto no presente estudo visa permitir que os índices de dependabilidade e as falhas futuras de sistemas elétricos, eletrônicos e programáveis sejam estimados utilizando-se dados já disponíveis de falhas e manutenções passadas. Para tanto, foram empregadas técnicas como processos estocásticos, filtragem Kalman e modelos de incorporação de dados de histórico preconizados no padrão internacional RIAC-HDBK-217Plus. Como principal conclusão do presente trabalho, é possível ressaltar que foi possível atingir, com o modelo proposto, o objetivo de suporte à manutenção preditiva de sistemas elétricos, eletrônicos e programáveis a partir do uso de dados preexistentes de histórico operacional; no entanto, foram constatadas limitações no grau de utilização prática do modelo em situações nas quais a quantidade dos dados de histórico disponíveis para consulta é pequena. / With the increased use of electrical, electronic and programmable systems in various application fields such as entertainment, financial transactions, power distribution, industrial process control and signaling and control of transportation modes, it is essential for the maintenance policies used in those systems to be able to minimize the costs of any faults that may adversely affect the services provided. Over the past decades, the use of predictive maintenance techniques has shown to be a viable and promising approach to detect faults before they actually occur in systems used in different application fields. Considering that a significant part of the recent scientific research in the area of predictive maintenance usually demands high-cost infrastructure to be installed to support the acquisition of all the data that will be used to calculate the prediction of future faults of a system, the model proposed within this study was designed to allow both dependability levels and future faults of electrical, electronic and programmable systems to be estimated using past faults and maintenance data that may already be available. For this purpose, techniques such as stochastic processes, Kalman filtering and models prescribed within the international standard RIAC-HDBK-217Plus to incorporate history data to dependability calculation were used. As the main conclusion of this study, it is possible to highlight that the main objective of the model proposed, related to its ability to support predictive maintenance of electrical, electronic and programmable systems through the use of pre-existing operating history data, has been reached; nevertheless, limitation of practical use of the model was verified in situations in which not enough operating data is available.

Com o aumento do uso de sistemas elétricos, eletrônicos e programáveis em aplicações de diversos domínios, tais como entretenimento, realização de transações financeiras, distribuição de energia elétrica, controle de processos industriais e sinalização e controle em transporte de passageiros e carga, é essencial que as políticas de manutenção utilizadas sejam capazes de minimizar os custos associados a eventuais falhas que afetem negativamente os serviços providos. Ao longo das últimas décadas, foi sedimentada a tendência de que a adoção de técnicas de manutenção preditiva representa uma das abordagens mais viáveis e promissoras para que falhas de sistemas utilizados em diversas aplicações possam ser detectadas antes de elas efetivamente ocorrerem. Considerando-se que uma parcela significativa dos estudos recentes na área de manutenção preditiva de sistemas apresenta como limitação o custo elevado para se instalar uma infraestrutura específica para realizar a coleta de dados que serão usados para dar suporte à predição das falhas futuras de um sistema, o modelo proposto no presente estudo visa permitir que os índices de dependabilidade e as falhas futuras de sistemas elétricos, eletrônicos e programáveis sejam estimados utilizando-se dados já disponíveis de falhas e manutenções passadas. Para tanto, foram empregadas técnicas como processos estocásticos, filtragem Kalman e modelos de incorporação de dados de histórico preconizados no padrão internacional RIAC-HDBK-217Plus. Como principal conclusão do presente trabalho, é possível ressaltar que foi possível atingir, com o modelo proposto, o objetivo de suporte à manutenção preditiva de sistemas elétricos, eletrônicos e programáveis a partir do uso de dados preexistentes de histórico operacional; no entanto, foram constatadas limitações no grau de utilização prática do modelo em situações nas quais a quantidade dos dados de histórico disponíveis para consulta é pequena. / With the increased use of electrical, electronic and programmable systems in various application fields such as entertainment, financial transactions, power distribution, industrial process control and signaling and control of transportation modes, it is essential for the maintenance policies used in those systems to be able to minimize the costs of any faults that may adversely affect the services provided. Over the past decades, the use of predictive maintenance techniques has shown to be a viable and promising approach to detect faults before they actually occur in systems used in different application fields. Considering that a significant part of the recent scientific research in the area of predictive maintenance usually demands high-cost infrastructure to be installed to support the acquisition of all the data that will be used to calculate the prediction of future faults of a system, the model proposed within this study was designed to allow both dependability levels and future faults of electrical, electronic and programmable systems to be estimated using past faults and maintenance data that may already be available. For this purpose, techniques such as stochastic processes, Kalman filtering and models prescribed within the international standard RIAC-HDBK-217Plus to incorporate history data to dependability calculation were used. As the main conclusion of this study, it is possible to highlight that the main objective of the model proposed, related to its ability to support predictive maintenance of electrical, electronic and programmable systems through the use of pre-existing operating history data, has been reached; nevertheless, limitation of practical use of the model was verified in situations in which not enough operating data is available.

Αντικείμενο της διπλωματικής εργασίας είναι η εφαρμογή Εξελικτικών Μεθόδων, βασισμένων, στους Γενετικούς Αλγόριθμους, στο πρόβλημα της επιλογής της τάξης και της αναγνώρισης των παραμέτρων γραμμικών συστημάτων και ειδικότερα Αυτοανάδρομων Κινούμενου Μέσου όρου Διαδικασιών ARMA (Autoregressive Moving Average Processes). Οι Γενετικοί Αλγόριθμοι είναι αλγόριθμοι αναζήτησης που βασίζονται στις αρχές της εξέλιξης που παρατηρούνται στη φύση και γίνονται όλο και περισσότερο γνωστοί χάριν της ικανότητά τους να λύνουν δύσκολα προβλήματα. Οι ΓΑ χαρακτηρίζονται από την απλότητα και την κομψότητά τους ως ‘γεροί’ αλγόριθμοι αναζήτησης, καθώς επίσης και από τη ικανότητά τους να ανακαλύπτουν γρήγορα τις καλές λύσεις δύσκολων και κυρίως μεγάλης διάστασης προβλημάτων. Το θεμελιώδες πρόβλημα της επιλογής της τάξης και της αναγνώρισης των παραμέτρων ενός μοντέλου, έχει αντιμετωπιστεί με επιτυχία με τη χρήση της θεωρίας Διαμερισμού Πολλών Μοντέλων (Multi Model Partitioning -MMP) του Λαϊνιώτη. Βασισμένη στην εκ των υστέρων επιλογή του συνόλου των υποψηφίων μοντέλων, η μέθοδος αυτή δίνει βέλτιστες λύσεις - ή σχεδόν βέλτιστες, όταν η πραγματική τάξη του μοντέλου δεν ανήκει στον αρχικό πληθυσμό των υποψηφίων μοντέλων. Το μειονέκτημα της εξάρτησης από την εκ των υστέρων επιλογή των υποψηφίων μοντέλων μπορεί να αντιμετωπιστεί με τη χρήση τεχνικών φυσικής επιλογής, όπως οι Γενετικοί Αλγόριθμοι, οι οποίοι αποτελούν μία από τις πιο γνωστές και αποτελεσματικές μεθόδους αναζήτησης και βελτιστοποίησης. Η εξελικτική μέθοδος, που παρουσιάζεται στην εργασία αυτή, συνδυάζει την αποτελεσματικότητα της MMP θεωρίας με την ευρωστία των Γενετικών Αλγορίθμων με σκοπό τη δημιουργία μίας νέας γενιάς πολυδιάστατων φίλτρων διαμερισμού. Η δομή των φίλτρων αυτών μεταβάλλεται διαρκώς για να ταιριάζει κάθε φορά με ένα δεδομένο σύνολο μοντέλων, τα οποία προσδιορίζονται δυναμικά και on-line με τη χρήση ενός κατάλληλα σχεδιασμένου ΓΑ. Παρά του ότι η κωδικοποίηση των παραμέτρων είναι σύνθετη, τα πειραματικά αποτελέσματα έδειξαν ότι ο προτεινόμενος αλγόριθμος επιτυγχάνει καλύτερα αποτελέσματα, σε σύγκριση με τους συμβατικούς αλγορίθμους αναγνώρισης συστήματος, αφού έχει τη δυνατότητα να εξερευνά ολόκληρο το χώρο τιμών των παραμέτρων. Επιπλέον, η εξέλιξη του αρχικού πληθυσμού καταλήγει σε εύρεση της πραγματικής τάξης του μοντέλου του συστήματος ακόμα και στην περίπτωση όπου η πραγματική τάξη δεν ανήκει στην τράπεζα μοντέλων του αρχικού πληθυσμού. Η υλοποίηση του αλγόριθμου έγινε σε παράλληλο περιβάλλον, αφού τόσο το Multi Model Adaptive Filter (MMAF) όσο και οι Γενετικοί Αλγόριθμοι είναι από τη φύση τους παράλληλα δομημένοι, οδηγώντας έτσι στη βελτίωση της ταχύτητας του αλγορίθμου. Με σκοπό να επιτευχθεί επιπλέον βελτίωση του αλγορίθμου τόσο ως προς την αύξηση της ταχύτητας του όσο και την ποιότητα της εξέλιξης των πληθυσμών των ΓΑ, έγινε χρήση ενός επιπλέον Γενετικού Αλγορίθμου ο οποίος προσδιόρισε τις τιμές των παραμέτρων των ΓΑ που υλοποιούν την υβριδική εξελικτική μέθοδο. Ο Μετά-Γενετικός αλγόριθμος προσδιόρισε το Μέγεθος του Πληθυσμού, την Πιθανότητα Μετάλλαξης και Διασταύρωσης των παράλληλων ΓΑ. Από τα πειραματικά αποτελέσματα που προέκυψαν μπορεί κάποιος εύκολα να καταλήξει στο συμπέρασμα ότι ο ΜΓΑ καταφέρνει να επιλέξει τις βέλτιστες τιμές για τις βασικές γενετικές παραμέτρους με αποτέλεσμα η όλη διαδικασία να μπορεί να αυτοματοποιηθεί και να είναι πλήρως προσαρμόσιμη σε οποιαδήποτε αλλαγή συμβεί στο περιβάλλον εφαρμογής του ΜΓΑ. / -

In both developed and emerging-economies, major cities continue to experience increasing traffic congestion. To address this issue, complex Traffic Management Systems (TMS) are employed in recent years to help manage traffic. These systems fuse traffic-surveillance-related information from a variety of sensors deployed across traffic networks. A TMS requires real-time information to make effective control decisions and to deliver trustworthy information to users, such as travel time, congestion level, etc. There are three fundamental inputs required by TMS, namely, traffic volume, vehicular speed, and traffic density. Using conventional traffic loop detectors one can directly measure flow and velocity. However, traffic density is more difficult to measure. The situation becomes more difficult for multi-lane motorways due to drivers lane-change behaviour. This research investigates statistical modelling and analysis of traffic flow. It contributes to the literature of transportation and traffic management and research in several aspects. First, it takes into account lane-changes in traffic modelling through incorporating a Markov chain model to describe the drivers lane-change behaviour. Secondly, the lane change probabilities between two adjacent lanes are not assumed to be fixed but rather they depend on the current traffic condition. A discrete choice model is used to capture drivers lane choice behaviour. The drivers choice probabilities are modelled by several traffic-condition related attributes such as vehicle time headway, traffic density and speed. This results in a highly nonlinear state equation for traffic density. To address the issue of high nonlinearity of the state space model, the EKF and UKF is used to estimate the traffic density recursively. In addition, a new transformation approach has been proposed to transform the observation equation from a nonlinear form to a linear one so that the potential approximation in the EKF & UKF can be avoided. Numerical studies have been conducted to investigate the performance of the developed method. The proposed method outperformed the existing methods for traffic density estimation in simulation studies. Furthermore, it is shown that the computational cost for updating the estimate of traffic densities for a multi-lane motorway is kept at a minimum so that online applications are feasible in practice. Consequently the traffic densities can be monitored and the relevant information can be fed into the traffic management system of interest.

Apresenta-se o desenvolvimento de modelos matemáticos e algoritmos de fusão sensorial para navegação terrestre usando uma unidade de medição inercial (UMI) de baixo desempenho e o Filtro Estendido de Kalman. Os modelos foram desenvolvidos com base nos sistemas de navegação inercial strapdown (SNIS). O termo baixo desempenho refere-se à UMIs que por si só não são capazes de efetuar o auto- alinhamento por girocompassing. A incapacidade de se navegar utilizando apenas uma UMI de baixo desempenho motiva a investigação de técnicas que permitam aumentar o grau de precisão do SNIS com a utilização de sensores adicionais. Esta tese descreve o desenvolvimento do modelo completo de uma fusão sensorial para a navegação inercial de um veículo terrestre usando uma UMI de baixo desempenho, um hodômetro e uma bússola eletrônica. Marcas topográficas (landmarks) foram instaladas ao longo da trajetória de teste para se medir o erro da estimativa de posição nesses pontos. Apresenta-se o desenvolvimento do Filtro de Kalman Adaptativo Suavizado (FKAS), que estima conjuntamente os estados e o erro dos estados estimados do sistema de fusão sensorial. Descreve-se um critério quantitativo que emprega as incertezas de posição estimadas pelo FKAS para se determinar a priori, dado os sensores disponíveis, o intervalo de tempo máximo que se pode navegar dentro de uma margem de confiabilidade desejada. Conjuntos reduzidos de landmarks são utilizados como sensores fictícios para testar o critério de confiabilidade proposto. Destacam-se ainda os modelos matemáticos aplicados à navegação terrestre, unificados neste trabalho. Os resultados obtidos mostram que, contando somente com os sensores inerciais de baixo desempenho, a navegação terrestre torna-se inviável após algumas dezenas de segundos. Usando os mesmos sensores inerciais, a fusão sensorial produziu resultados muito superiores, permitindo reconstruir trajetórias com deslocamentos da ordem de 2,7 km (ou 15 minutos) com erro final de estimativa de posição da ordem de 3 m. / This work presents the development of the mathematical models and the algorithms of a sensor fusion system for terrestrial navigation using a low-grade inertial measurement unit (IMU) and the Extended Kalman Filter. The models were developed on the basis of the strapdown inertial navigation systems (SINS). Low-grade designates an IMU that is not able to perform girocompassing self-alignment. The impossibility of navigating relying on a low performance IMU is the motivation for investigating techniques to improve the SINS accuracy with the use of additional sensors. This thesis describes the development of a comprehensive model of a sensor fusion for the inertial navigation of a ground vehicle using a low-grade IMU, an odometer and an electronic compass. Landmarks were placed along the test trajectory in order to allow the measurement of the error of the position estimation at these points. It is presented the development of the Smoothed Adaptive Kalman Filter (SAKF), which jointly estimates the states and the errors of the estimated states of the sensor fusion system. It is presented a quantitative criteria which employs the position uncertainties estimated by SAKF in order to determine - given the available sensors, the maximum time interval that one can navigate within a desired reliability. Reduced sets of landmarks are used as fictitious sensors to test the proposed reliability criterion. Also noteworthy are the mathematical models applied to terrestrial navigation that were unified in this work. The results show that, only relying on the low performance inertial sensors, the terrestrial navigation becomes impracticable after few tens of seconds. Using the same inertial sensors, the sensor fusion produced far better results, allowing the reconstruction of trajectories with displacements of about 2.7 km (or 15 minutes) with a final error of position estimation of about 3 m.

Orientadores: Renato da Rocha Lopes, João Marcos Travassos Romano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-13T19:18:48Z (GMT). No. of bitstreams: 1 Loiola_MuriloBellezoni_D.pdf: 1970092 bytes, checksum: 28591fb8ccc8eb5f6eb64dfa626f4241 (MD5) Previous issue date: 2009 / Resumo: Neste trabalho utilizamos filtros de Kalman para estimar canais de comunicação sem fio variantes no tempo em sistemas com múltiplas entradas e múltiplas saídas. Primeiramente, propusemos um estimador ótimo (no sentido de minimização do erro quadrático médio de estimação) para rastrear canais planos em sistemas utilizando códigos espaço-temporais ortogonais por blocos. Graças à ortogonalidade destes códigos, foi possível simplificar as equações do filtro de Kalman. Mostramos que as estimativas fornecidas pelo estimador proposto correspondem a somas ponderadas de estimativas instantâneas de máxima verossimilhança do canal. Ainda para este sistema, propusemos um filtro de Kalman em estado estacionário para modulações de módulo constante. O filtro em estado estacionário tem desempenho semelhante ao do filtro de Kalman ótimo, embora necessite apenas de uma fração dos cálculos envolvidos. Em seguida, propusemos um receptor baseado no filtro de Kalman estendido para realizar conjuntamente as tarefas de estimação de canais seletivos em freqüência e detecção de sinais em sistemas com múltiplas entradas, múltiplas saídas (MIMO, do inglês multiple-input, multiple-output) e multiplexação espacial. Por fim, adaptamos este estimador conjunto para incorporá-lo a um receptor turbo. Desta maneira, o estimador conjunto pode aproveitar a redundância introduzida pela codificação de canal para aprimorar as estimativas dos coeficientes do canal e dos símbolos transmitidos por meio de um processo iterativo / Abstract: In this work we use Kalman filters to estimate time-varying wireless channels in multiple-input, multiple-output (MIMO) systems. First, we propose an optimal estimator (in the minimum mean squared error sense) to track flat channels in orthogonal space-time block coded systems. Due to the orthogonality inherent to these codes, the Kalman filter equations can be simplified. We also show that the channel estimates provided by the proposed estimator correspond to weighted sums of instantaneous maximum likelihood channel estimates. For constant modulus signal constellations, we propose a steady-state Kalman filter. The proposed steady-state Kalman filter suffers negligible performance degradation compared to the optimal Kalman filter while requiring just a fraction of its complexity. After that, we propose an extended Kalman filter-based receiver that jointly performs the estimation of time-varying frequency-selective MIMO channels and the detection of transmitted signals in spatial multiplexing systems. Finally, we adapt this joint estimator to a turbo receiver. Therefore, the joint estimator can benefit from the error correction capabilities of channel codes to iteratively improve channel and signal estimates / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

L'utilisation de caméras infrarouges bas coûts pour la surveillance long-terme d'infrastructures est prometteuse grâce aux dernières avancées technologiques du domaine. Une mesure précise de la température des surfaces observées in-situ se heurte au manque de connaissance des propriétés radiatives de la scène. L'utilisation d'une instrumentation multi-capteurs permet d'affiner le modèle de mesure afin d'obtenir une estimation plus précise de la température. A contrario, il est montré qu'il est toujours possible d'exploiter des données climatiques en ligne pour pallier un manque de capteur. Des méthodes bayésiennes d'estimation conjointe d'émissivité et de température sont ensuite développées et comparées aux méthodes de la littérature. Un simulateur d'échanges radiatifs diffus de scènes 3D a été implémenté afin de tester ces différentes méthodes. Ce logiciel utilise l'accélération matérielle de la machine pour réduire les temps de calcul. Les résultats numériques obtenus mettent en perspective une utilisation avancée de la thermographie infrarouge multi-spectrale pour la surveillance de structures. Cette estimation conjointe permet alors d'obtenir un estimé de la température par thermographie infrarouge avec une incertitude connue. / The latest technological improvements in low-cost infrared cameras have brought new opportunities for long-term infrastructures monitoring. The accurate measurement of surfaces' temperatures is facing the lack of knowledge of radiatives properties of the scene. By using multi-sensors instrumentation, the measurement model can be refined to get a better estimate of the temperature. To overcome a lack of sensors instrumentation, it is shown that online and free available climatic data can be used. Then, Bayesian methods to estimate simultaneously the emissivity and temperature have been developed and compared to literature's methods. A radiative exchange simulator of 3D scenes have been developed to compare those different methods on numerical data. This software uses the hardware acceleration as well as a GPGPU approach to reduce the computation time. As a consequence, obtained numerical results emphasized an advanced use of multi-spectral infrared thermography for the monitoring of structures. This simultaneous estimation enables to have an estimate of the temperature by infrared thermography with a known uncertainty.

Stroke is the largest cause of severe adult complex disability, caused when the blood supply to the brain is interrupted, either by a clot or a burst blood vessel. It is characterised by deficiencies in movement and balance, changes in sensation, impaired motor control and muscle tone, and bone deformity. Clinically applied stroke management relies heavily on the observational opinion of healthcare workers. Despite the proven validity of a few clinical outcome measures, they remain subjective and inconsistent, and suffer from a lack of standardisation. Motion capture of the upper limb has also been used in specialised laboratories to obtain accurate and objective information, and monitor progress in rehabilitation. However, it is unsuitable in environments that are accessible to stroke patients (for example at patients’ homes or stroke clubs), due to the high cost, special set-up and calibration requirements. The aim of this research project was to validate and assess the sensitivity of a relatively low cost, wearable, compact and easy-to-use monitoring system, which uses inertial sensors in order to obtain detailed analysis of the forearm during simple functional exercises, typically used in rehabilitation. Forearm linear and rotational motion were characterised for certain movements on four healthy subjects and a stroke patient using a motion capture system. This provided accuracy and sensitivity specifications for the wearable monitoring system. With basic signal pre-processing, the wearable system was found to report reliably on acceleration, angular velocity and orientation, with varying degrees of confidence. Integration drift errors in the estimation of linear velocity were unresolved. These errors were not straightforward to eliminate due to the varying position of the sensor accelerometer relative to gravity over time. The cyclic nature of rehabilitation exercises was exploited to improve the reliability of velocity estimation with model-based Kalman filtering, and least squares optimisation techniques. Both signal processing methods resulted in an encouraging reduction of the integration drift in velocity. Improved sensor information could provide a visual display of the movement, or determine kinematic quantities relevant to the exercise performance. Hence, the system could potentially be used to objectively inform patients and physiotherapists about progress, increasing patient motivation and improving consistency in assessment and reporting of outcomes.

The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control. This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach. The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties. The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations. Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.