In this dissertation novel methods are proposed for communicating in interference limited environments as well as detecting such interference. The methods include introducing redundancies into multicarrier signals to make them more robust, applying a novel filtering structure for mitigating radar interference to orthogonal frequency division multiplexing (OFDM) signals and for exploiting the cyclostationary nature of signals to whiten the spectrum in blind signal detection. Data symbols are repeated in both time and frequency across orthogonal frequency division multiplexing (OFDM) symbols, creating a cyclostationary nature in the signal. A Frequency Shift (FRESH) filter can then be applied to the cyclostationary signal, which is the optimal filter and is able to reject interference much better than a time-invariant filter such as the Wiener filter. A novel time-varying FRESH filter (TV-FRESH) filter is developed and its Minimum Mean Squared Error (MMSE) filter weights are found. The repetition of data symbols and their optimal combining with the TV-FRESH filter creates an effect of improving the Bit Error Rate (BER) at the receiver, similar to an error correcting code. The important distinction for the paramorphic method is that it is designed to operate within cyclostationary interference, and simulation results show that the symbol repetition can outperform other error correcting codes. Simulated annealing is used to optimize the signaling parameters, and results show that a balance between the symbol repetition and error correcting codes produces a better BER for the same spectral efficiency than what either method could have achieved alone. The TV-FRESH filter is applied to a pulsed chirp radar signal, demonstrating a new tool to use in radar and OFDM co-existence. The TV-FRESH filter applies a set of filter weights in a periodically time-varying fashion. The traditional FRESH filter is periodically time-varying due to the periodicities of the frequency shifters, but applies time-invariant filters after optimally combine any spectral redundancies in the signal. The time segmentation of the TV-FRESH filter allows spectral redundancies of the radar signal to be exploited across time due to its deterministic nature. The TV-FRESH filter improves the rejection of the radar signal as compared to the traditional FRESH filter under the simulation scenarios, improving the SINR and BER at the output of the filter. The improvement in performance comes at the cost of additional filtering complexity. A time-varying whitening filter is applied to blindly detect interference which overlaps with the desired signal in frequency. Where a time-invariant whitening filter shapes the output spectrum based on the power levels, the proposed time-varying whitener whitens the output spectrum based on the spectral redundancy in the desired signal. This allows signals which do not share the same cyclostationary properties to pass through the filter, improving the sensitivity of the algorithm and producing higher detection rates for the same probability of false alarm as compared to the time-invariant whitener. / Ph. D. / This dissertation proposes novel methods for building robust wireless communication links which can be used to improve their reliability and resilience while under interference. Wireless interference comes from many sources, including other wireless transmitters in the area or devices which emit electromagnetic waves such as microwaves. Interference reduces the quality of a wireless link and depending on the type and severity may make it impossible to reliably receive information. The contributions are both for communicating under interference and being able to detect interference. A novel method for increasing the redundancy in a wireless link is proposed which improves the resiliency of a wireless link. By transmitting additional copies of the desired information the wireless receiver is able to better estimate the original transmitted signal. The digital receiver structure is proposed to optimally combine the redundant information, and simulation results are used to show its improvement over other analogous methods. The second contribution applies a novel digital filter for mitigating interference from a radar signal to an Orthogonal Frequency Division Multiplexing (OFDM) signal, similar to the one which is being used in Long Term Evolution (LTE) mobile phones. Simulation results show that the proposed method out performs other digital filters at the most of additional complexity. The third contribution applies a digital filter and trains it such that the output of the filter can be used to detect the presence of interference. An algorithm which detects interference can tip off an appropriate response, and as such is important to reliable wireless communications. Simulation results are used to show that the proposed method produces a higher probability of detection while reducing the false alarm rate as compared to a similar digital filter trained to produce the same effect.

Echo in telephones is generally undesirable but inevitable. There are two possible sources of echo in a telephone system. The impedance mismatch in hybrids generates network (electric) echo. The acoustic coupling between loudspeaker and microphone, in hands-free telephones, produces acoustic echo. Echo cancelers are used to control these echoes. In this thesis, we analyze the Least Mean Squares (LMS), Normalized LMS (NLMS), Recursive Least Squares (RLS), and Subband NLMS (SNLMS) algorithms, and evaluate their performance as acoustic and network echo cancelers. The algorithms are compared based on their convergence rate, steady state echo return loss (ERL), and complexity of implementation. While LMS is simple, its convergence rate is dependent on the eigenvalue spread of the signal. In particular, it converges slowly with speech as input. This problem is mitigated in NLMS. The complexity of NLMS is comparable to that of LMS. The convergence rate of RLS is independent of the eigenvalue spread, and it has the fastest convergence. On the other hand, RLS is highly computation intensive. Among the four algorithms considered here, SNLMS has the least complexity of implementation, as well as the slowest rate of convergence. Switching between the NLMS and SNLMS algorithms is used to achieve fast convergence with low computational requirements. For a given computational power, it is shown that switching between algorithms can give better performance than using either of the two algorithms exclusively, especially in rooms with long reverberation times. We also discuss various implementation issues associated with an integrated echo cancellation system, such as double-talk detection, finite precision effects, nonlinear processing, and howling detection and control. The use of a second adaptive filter is proposed, to reduce near-end ambient noise. Simulation results indicate that this approach can reduce the ambient noise by about 20 dB. A configuration is presented for the real time single-chip DSP implementation of acoustic and network echo cancelers, and an interface between the echo canceler and the telephone is proposed. Finally, some results obtained from simulations and implementations of individual modules, on the TMS320C31 and ADSP 2181 processors, are reported. The real time NLMS DSP implementations provide 15 dB of echo return loss. / Master of Science

Neste trabalho uma nova estratégia de combinação de filtros adaptativos é apresentada e estudada. Inspirada por esquemas incrementais e filtragem adaptativa cooperativa, a combinação convexa usual de filtros em paralelo e independentes é reestruturada como uma configuração série-cooperativa, sem aumento da complexidade computacional. Dois novos algoritmos são projetados utilizando Recursive Least-Squares (RLS) e Least-Mean-Squares (LMS) como subfiltros que compõem a combinação. Para avaliar a performance da estrutura incremental, uma análise de média quadrática é realizada. Esta é feita assumindo que os combinadores têm valores fixos, de forma a permitir o estudo da universalidade da estrutura desacoplada da dinâmica do supervisor. As simulações realizadas mostram uma boa concordância com o modelo teórico obtido. / In this work a new strategy for combination of adaptive filters is introduced and studied. Inspired by incremental schemes and cooperative adaptive filtering, the standard convex combination of parallel-independent filters is rearranged into a series-cooperative configuration, while preserving computational complexity. Two new algorithms are derived employing Recursive Least-Squares (RLS) and Least-Mean-Squares (LMS) algorithms as the component filters. In order to assess the performance of the incremental structure, tracking and steady-state mean-square analysis is derived. The analysis is carried out assuming the combiners are fixed, so that the universality of the new structure may be studied decoupled from the supervisor\'s dynamics. The resulting analytical model shows good agreement with simulation results.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-06-23T20:59:51Z No. of bitstreams: 1 CharlesSilvaNahuz.pdf: 2149704 bytes, checksum: 650e374d99de26e3390d88bf0e7ac78a (MD5) / Made available in DSpace on 2017-06-23T20:59:51Z (GMT). No. of bitstreams: 1 CharlesSilvaNahuz.pdf: 2149704 bytes, checksum: 650e374d99de26e3390d88bf0e7ac78a (MD5) Previous issue date: 2016-03-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / In this paper, implemented a new lter similar to the LMS, but, with a coast function based in the sum of the error. As a result, we obtain a very simple function, producing a rapid convergence and a small mismatch when compared with the LMS algorithm and other algorithms. The adaptive lter is based on non-linear functions such as estimation of the gradient of a surface performance. We use the gradient algorithm to update the weights. this update is based on high-order statistics to obtain information about the signs involved in the process, in order to improve the performace of the adaptive lter. Derive the equations based on Taylor series of non-linear functions, to achieve the criteria that ensures their convergence. We also do a weight vector covariance study in steady state and determine the equations that calculate the time constants in an adaptive process. Here the algorithm proposed, which uses a cost function and were made simulacoes Monte Carlo with real signals to validate the theory presented. In this role the α coefficients have been optimized to provide increased stability and better performance in its convergence speed. / Neste trabalho, implementamos um novo filtro semelhante ao LMS, porém, com uma função de custo baseada na soma do erro. Como resultado, obtemos uma função bastante simples, produzindo uma rápida convergência e um pequeno desajuste quando comparado com o algoritmo LMS e com outros algoritmos. O filtro adaptativo é baseado em funções não lineares como estimativa do gradiente de uma superfície de desempenho. Utilizamos o gradiente do algoritmo para atualização dos pesos. Essa atualização baseia-se nas estatísticas de alta ordem para obtenção de informações sobre os sinais envolvidos no processo, com o objetivo de melhorar a performance do filtro adaptativo. As equações foram derivadas e baseadas em séries de Taylor das funções não lineares, para obtenção dos critérios que garante a sua convergência. Também fazemos um estudo da covariância do vetor peso em regime estacionário e determinamos as equações que calculam as constantes de tempo em um processo adaptativo. Apresentamos o algoritmo proposto, que utiliza uma função de custo onde foram feitas simulações de Monte Carlo com sinais reais para validar a teoria apresentada. Nessa função os coe cientes αk foram otimizados para dar maior estabilidade e melhor desempenho na sua velocidade de convergência.

<p>Brain-machine interfaces (BMI) are systems which connect brains directly to machines or computers for communication. BMI-controlled prosthetic devices use algorithms to decode neuronal recordings into movement commands. These algorithms operate using models of how recorded neuronal signals relate to desired movements, called models of tuning. Models of tuning have typically been linear in prior work, due to the simplicity and speed of the algorithms used with them. Neuronal tuning has been shown to slowly change over time, but most prior work do not adapt tuning models to these changes. Furthermore, extracellular electrical recordings of neurons' action potentials slowly change over time, impairing the preprocessing step of spike-sorting, during which the neurons responsible for recorded action potentials are identified.</p> <p></p> <p>This dissertation presents a non-linear adaptive Bayesian filter and an adaptive spike-sorting method for BMI decoding. The adaptive filter consists of the n-th order unscented Kalman filter and Bayesian regression self-training updates. The unscented Kalman filter estimates desired prosthetic movements using a non-linear model of tuning as its observation model. The model is quadratic with terms for position, velocity, distance from center of workspace, and velocity magnitude. The tuning model relates neuronal activity to movements at multiple time offsets simultaneously, and the movement model of the filter is an order n autoregressive model.</p> <p>To adapt the tuning model parameters to changes in the brain, Bayesian regression self-training updates are performed periodically. Tuning model parameters are stored as probability distributions instead of point estimates. Bayesian regression uses the previous model parameters as priors and calculates the posteriors of the regression between filter outputs, which are assumed to be the desired movements, and neuronal recordings. Before each update, filter outputs are smoothed using a Kalman smoother, and tuning model parameters are passed through a transition model describing how parameters change over time. Two variants of Bayesian regression are presented: one uses a joint distribution for the model parameters which allows analytical inference, and the other uses a more flexible factorized distribution that requires approximate inference using variational Bayes.</p> <p>To adapt spike-sorting parameters to changes in spike waveforms, variational Bayesian Gaussian mixture clustering updates are used to update the waveform clustering used to calculate these parameters. This Bayesian extension of expectation-maximization clustering uses the previous clustering parameters as priors and computes the new parameters as posteriors. The use of priors allows tracking of clustering parameters over time and facilitates fast convergence.</p> <p>To evaluate the proposed methods, experiments were performed with 3 Rhesus monkeys implanted with micro-wire electrode arrays in arm-related areas of the cortex. Off-line reconstructions and on-line, closed-loop experiments with brain-control show that the n-th order unscented Kalman filter is more accurate than previous linear methods. Closed-loop experiments over 29 days show that Bayesian regression self-training helps maintain control accuracy. Experiments on synthetic data show that Bayesian regression self-training can be applied to other tracking problems with changing observation models. Bayesian clustering updates on synthetic and neuronal data demonstrate tracking of cluster and waveform changes. These results indicate the proposed methods improve the accuracy and robustness of BMIs for prosthetic devices, bringing BMI-controlled prosthetics closer to clinical use.</p> / Dissertation

A evolução tecnológica dos sensores, da eletrônica e dos sistemas embarcados melhorou o desempenho, a confiabilidade e a robustez dos sistemas assim como as atividades de manutenção, em especial, as de manutenção proativa. Estes avanços tecnológicos possibilitaram uma nova visão sobre as práticas de manutenção existentes. A expansão das áreas de processamento de sinais e inteligência artificial proporcionou novas abordagens aos sistemas de controle, promovendo a criação de novos modelos de confiabilidade e disponibilidade de equipamentos e sistemas. Além disso, aumentou a precisão no reconhecimento de padrões de falhas, ampliou a avaliação e o diagnóstico de danos em equipamentos e sistemas, e adicionou inteligência aos sistemas de manutenção existentes. Diversas técnicas de processamento de sinais (tais como a transformada de Fourier), de inteligência artificial (as redes neurais artificiais e a lógica nebulosa, por exemplo) e de filtragem adaptativa (os filtros adaptativos, como exemplo) já são utilizadas com sucesso para detectar e prevenir falhas em vários tipos de equipamentos. Os sistemas de manutenção que fazem uso das técnicas de processamento de sinais e inteligência artificial, em conjunto, por exemplo, são conhecidos como sistemas de manutenção inteligente. Através desses sistemas, é possível monitorar as condições físicas, tomar decisões, efetuar ações de manutenção e fornecer diagnósticos precisos de falhas. Este trabalho aborda a implementação de um sistema de manutenção inteligente embarcado que usa a transformada wavelet packet e os mapas auto-organizáveis ou os filtros adaptativos para detectar, classificar e prever falhas em atuadores elétricos. A idéia principal deste trabalho é determinar qual destas ferramentas, mapas auto-organizáveis ou filtros adaptativos, é a mais adequada para o embarque. Espera-se com a implantação embarcada desse sistema de manutenção, por exemplo, evitar falhas nos atuadores e promover uma maior reutilização de peças. / The technological evolution of sensors, electronics, and embedded systems has improved the performance, reliability and robustness of systems as well the maintenance activities, especially the proactive maintenance. These technological advances have provided a new view about the existing maintenance practices. The expansion of signal processing and artificial intelligence has provided new approaches in industrial control systems leading to the proposal of new reliability and availability models for equipments and systems. Moreover, it has increased the precision in failure pattern recognition, has extended the assessment and diagnosis of damages in equipments and systems, and has added intelligence to existing maintenance systems. Several techniques for signal processing (such as Fourier transform), artificial intelligence (artificial neural networks, for example) and adaptive filtering (adaptive filters, as an example) are already used successfully to detect and prevent failures in several kinds of equipments. The maintenance systems that use, for example, the techniques for signal processing and artificial intelligence together are known as intelligent maintenance systems. It is possible to control the physical conditions, make decisions, perform maintenance activities and do accurate diagnosis of failures using those systems. This work presents the implementation of an embedded intelligent maintenance system using wavelet packet analysis and self organizing maps or adaptive filters for detection, classification, and prediction of failures in electrical actuators. The main idea is to determine which of these tools, self-organizing maps or adaptive filters, is the most suitable for the implementation in embedded systems. It is expected that with the implementation of this maintenance system, failures in actuators are avoided, and that a greater reuse of parts is achieved.

A evolução tecnológica dos sensores, da eletrônica e dos sistemas embarcados melhorou o desempenho, a confiabilidade e a robustez dos sistemas assim como as atividades de manutenção, em especial, as de manutenção proativa. Estes avanços tecnológicos possibilitaram uma nova visão sobre as práticas de manutenção existentes. A expansão das áreas de processamento de sinais e inteligência artificial proporcionou novas abordagens aos sistemas de controle, promovendo a criação de novos modelos de confiabilidade e disponibilidade de equipamentos e sistemas. Além disso, aumentou a precisão no reconhecimento de padrões de falhas, ampliou a avaliação e o diagnóstico de danos em equipamentos e sistemas, e adicionou inteligência aos sistemas de manutenção existentes. Diversas técnicas de processamento de sinais (tais como a transformada de Fourier), de inteligência artificial (as redes neurais artificiais e a lógica nebulosa, por exemplo) e de filtragem adaptativa (os filtros adaptativos, como exemplo) já são utilizadas com sucesso para detectar e prevenir falhas em vários tipos de equipamentos. Os sistemas de manutenção que fazem uso das técnicas de processamento de sinais e inteligência artificial, em conjunto, por exemplo, são conhecidos como sistemas de manutenção inteligente. Através desses sistemas, é possível monitorar as condições físicas, tomar decisões, efetuar ações de manutenção e fornecer diagnósticos precisos de falhas. Este trabalho aborda a implementação de um sistema de manutenção inteligente embarcado que usa a transformada wavelet packet e os mapas auto-organizáveis ou os filtros adaptativos para detectar, classificar e prever falhas em atuadores elétricos. A idéia principal deste trabalho é determinar qual destas ferramentas, mapas auto-organizáveis ou filtros adaptativos, é a mais adequada para o embarque. Espera-se com a implantação embarcada desse sistema de manutenção, por exemplo, evitar falhas nos atuadores e promover uma maior reutilização de peças. / The technological evolution of sensors, electronics, and embedded systems has improved the performance, reliability and robustness of systems as well the maintenance activities, especially the proactive maintenance. These technological advances have provided a new view about the existing maintenance practices. The expansion of signal processing and artificial intelligence has provided new approaches in industrial control systems leading to the proposal of new reliability and availability models for equipments and systems. Moreover, it has increased the precision in failure pattern recognition, has extended the assessment and diagnosis of damages in equipments and systems, and has added intelligence to existing maintenance systems. Several techniques for signal processing (such as Fourier transform), artificial intelligence (artificial neural networks, for example) and adaptive filtering (adaptive filters, as an example) are already used successfully to detect and prevent failures in several kinds of equipments. The maintenance systems that use, for example, the techniques for signal processing and artificial intelligence together are known as intelligent maintenance systems. It is possible to control the physical conditions, make decisions, perform maintenance activities and do accurate diagnosis of failures using those systems. This work presents the implementation of an embedded intelligent maintenance system using wavelet packet analysis and self organizing maps or adaptive filters for detection, classification, and prediction of failures in electrical actuators. The main idea is to determine which of these tools, self-organizing maps or adaptive filters, is the most suitable for the implementation in embedded systems. It is expected that with the implementation of this maintenance system, failures in actuators are avoided, and that a greater reuse of parts is achieved.

A evolução tecnológica dos sensores, da eletrônica e dos sistemas embarcados melhorou o desempenho, a confiabilidade e a robustez dos sistemas assim como as atividades de manutenção, em especial, as de manutenção proativa. Estes avanços tecnológicos possibilitaram uma nova visão sobre as práticas de manutenção existentes. A expansão das áreas de processamento de sinais e inteligência artificial proporcionou novas abordagens aos sistemas de controle, promovendo a criação de novos modelos de confiabilidade e disponibilidade de equipamentos e sistemas. Além disso, aumentou a precisão no reconhecimento de padrões de falhas, ampliou a avaliação e o diagnóstico de danos em equipamentos e sistemas, e adicionou inteligência aos sistemas de manutenção existentes. Diversas técnicas de processamento de sinais (tais como a transformada de Fourier), de inteligência artificial (as redes neurais artificiais e a lógica nebulosa, por exemplo) e de filtragem adaptativa (os filtros adaptativos, como exemplo) já são utilizadas com sucesso para detectar e prevenir falhas em vários tipos de equipamentos. Os sistemas de manutenção que fazem uso das técnicas de processamento de sinais e inteligência artificial, em conjunto, por exemplo, são conhecidos como sistemas de manutenção inteligente. Através desses sistemas, é possível monitorar as condições físicas, tomar decisões, efetuar ações de manutenção e fornecer diagnósticos precisos de falhas. Este trabalho aborda a implementação de um sistema de manutenção inteligente embarcado que usa a transformada wavelet packet e os mapas auto-organizáveis ou os filtros adaptativos para detectar, classificar e prever falhas em atuadores elétricos. A idéia principal deste trabalho é determinar qual destas ferramentas, mapas auto-organizáveis ou filtros adaptativos, é a mais adequada para o embarque. Espera-se com a implantação embarcada desse sistema de manutenção, por exemplo, evitar falhas nos atuadores e promover uma maior reutilização de peças. / The technological evolution of sensors, electronics, and embedded systems has improved the performance, reliability and robustness of systems as well the maintenance activities, especially the proactive maintenance. These technological advances have provided a new view about the existing maintenance practices. The expansion of signal processing and artificial intelligence has provided new approaches in industrial control systems leading to the proposal of new reliability and availability models for equipments and systems. Moreover, it has increased the precision in failure pattern recognition, has extended the assessment and diagnosis of damages in equipments and systems, and has added intelligence to existing maintenance systems. Several techniques for signal processing (such as Fourier transform), artificial intelligence (artificial neural networks, for example) and adaptive filtering (adaptive filters, as an example) are already used successfully to detect and prevent failures in several kinds of equipments. The maintenance systems that use, for example, the techniques for signal processing and artificial intelligence together are known as intelligent maintenance systems. It is possible to control the physical conditions, make decisions, perform maintenance activities and do accurate diagnosis of failures using those systems. This work presents the implementation of an embedded intelligent maintenance system using wavelet packet analysis and self organizing maps or adaptive filters for detection, classification, and prediction of failures in electrical actuators. The main idea is to determine which of these tools, self-organizing maps or adaptive filters, is the most suitable for the implementation in embedded systems. It is expected that with the implementation of this maintenance system, failures in actuators are avoided, and that a greater reuse of parts is achieved.

Made available in DSpace on 2016-08-17T16:54:33Z (GMT). No. of bitstreams: 1 Tese Cristiane Cristina.pdf: 4404224 bytes, checksum: a68e5757bedc2d3d341a5937f100fe1f (MD5) Previous issue date: 2013-07-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In adaptive filtering many adaptive filter are based on the mean square error method (MSE). These filters were developed to improve convergence spedd with a lower misadjustment. The least mean square (LMS) and the recursive least square (RLS) algorithms have been the hallmark of adaptive filtering. In this work we develop adaptive algorithms based on the even powers of the error inspired in the recursive lest square (RLS) algorithm. Namely recursive nom quadratic (RNQ) algorithm. The ideas is based on Widrow s least mean square fourth (LMF) algorithm. Fisrt we derive equations based on a singal even power of the error in order to obtain criterions that guarantee convergence. We also determine equations that measure the misadjustment and the time constant of the adaptive process of the RNQ algorithm. We work also, toward making the algorithm less sensitive to the size of the error in na alternative direction, by proposing a cost function which is a sum of the even powers of the error. This second approach bring the error explicitly to the RLS algorithm formulation by proposing a new cost function that preserves the measnsquare-error (MSE) solution, but allows for the exploitation of higher order moments of the error to speedup the converge of the algorithm. The main goal this work is to create form first principles (new cost functions ) a mechanism to include instantaneous error information in the RLS algorithm, make it track better, and allow for the design of the forgetting factor. As we will see the key aspecto of our approach is to include the error in the Kalman gain that effectively controls the speed of adaptation of the RLS algorithm. / Em filtragem adaptativa, vários filtros são baseados no método do erro quadrático médio (do inglês, MSE- mean squared error ) e muitos desses foram desenvolvidos para obter uma convergência rápida com um menos desajuste. Os algoritmos mínimos quadrático médio (do inglês, LMS- least mean square ) e mínimos quadrados recursivos (do inglês, RLS- recursive least square ) foram um marco em filtragem adaptativa. Nesse trabalho apresentamos o desenvolvimento de uma família de algoritmos adaptativos baseados nas potências pares do erro, inspirado na dedução do algoritmo RLS padrão. Chamaremos esses novos algoritmos de recursivo não-quadrático (RNQ). A ideia básica é baseada na função de custo apresentada por Widrow no algoritmo mínimo quarto médio ( do inglês, LMF least mean square fourth). Inicialmente derivamos equações baseados em uma potência par do erro para obter critérios que garantam a convergência. Determinamos também, equações que definem o desajuste e o tempo de aprendizagem do processo de adaptação do algoritmo RNQ baseado em potência para arbitrária. Trabalhamos também, no sentido de tornar o algoritmo menos sensível ao tamanho do erro numa direção alternativa, propondo uma função de custo baseado na soma das potências pares do erro. Essa segunda abordagem torna explícito o papel do erro na formulação do RLS ao propor uma nova função de custo que preserve a solução MSE, mas permite a utilização dos momentos de alta ordem do erro para aumentar a velocidade de convergência do algoritmo. O principal objetivo do nosso trabalho é criar a partir dos primeiros princípios (novas funções de custo) um mecanismo para incluir informações de erro instantâneo no algoritmo RLS e torná-lo um seguidor melhor. Assim, o aspecto-chave dessa nova abordagem é incluir o erro no ganho de Kalman que controla efetivamente a velocidade de adaptação do algoritmo de RLS.

abstract: For the last 50 years, oscillator modeling in ranging systems has received considerable attention. Many components in a navigation system, such as the master oscillator driving the receiver system, as well the master oscillator in the transmitting system contribute significantly to timing errors. Algorithms in the navigation processor must be able to predict and compensate such errors to achieve a specified accuracy. While much work has been done on the fundamentals of these problems, the thinking on said problems has not progressed. On the hardware end, the designers of local oscillators focus on synthesized frequency and loop noise bandwidth. This does nothing to mitigate, or reduce frequency stability degradation in band. Similarly, there are not systematic methods to accommodate phase and frequency anomalies such as clock jumps. Phase locked loops are fundamentally control systems, and while control theory has had significant advancement over the last 30 years, the design of timekeeping sources has not advanced beyond classical control. On the software end, single or two state oscillator models are typically embedded in a Kalman Filter to alleviate time errors between the transmitter and receiver clock. Such models are appropriate for short term time accuracy, but insufficient for long term time accuracy. Additionally, flicker frequency noise may be present in oscillators, and it presents mathematical modeling complications. This work proposes novel H∞ control methods to address the shortcomings in the standard design of time-keeping phase locked loops. Such methods allow the designer to address frequency stability degradation as well as high phase/frequency dynamics. Additionally, finite-dimensional approximants of flicker frequency noise that are more representative of the truth system than the tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in a wide variety of operating environments, novel Banks of Adaptive Extended Kalman Filters are used to address both stochastic and dynamic uncertainty. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020