Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Application Of Cascade-Correlation Neural Networks In Developing Stock Selection Models For Global Equities

We investigate the potential of artificial neural networks (ANN) in the stock selection process of actively managed funds. Two ANN models are constructed to perform stock selection, using the Dow Jones (DJ) Sector Titans as the research database. The cascade-correlation algorithm of Fahlman and Lebiere (1990/1991) is combined with embedded learning rules, namely the backpropagation learning rule and the extended Kalman filter learning rule to forecast the cross-section of global equity returns. The main findings support the use of artificial neural networks for financial forecasting as an active portfolio management tool. In particular, fractile analysis and risk-adjusted return performance metrics provide evidence that the model trained via the extended Kalman filter rule had greater strength in identifying future top performers for global equities than the model trained via the backpropagation learning rule. There is no distinguishable difference between the performances of the bottom quartiles formed by both ANN models. The zero-investment portfolios formed by longing the top quartiles and simultaneously shorting the bottom quartiles or the market proxy exhibit statistically significant Jensen’s alpha and continues to accumulate positive returns over the out-of-sample period for both ANN models. On the other hand, the zero-investment portfolios formed by longing the bottom quartiles and simultaneously shorting the market proxy exhibit statistically significant Jensen’s alpha and continues to accumulate losses over the out-of-sample period for both ANN models. The implementation of the extended Kalman filter rule in training artificial neural networks for applications involving noisy financial data is recommended

WAVELET BASED FEATURE EXTRACTOR AND ANN BASED CLASSIFIER FOR OPTIMAL ECG INTERPRETATION

The heart plays the most vital role of supplying nutrients and oxygen in any organism. Any abnormality in its function renders the body to many complications which may sometimes even lead to death. Hence, timely and early diagnosis of any abnormality is extremely important. Another requirement of the hour is the Automatic detection. Several techniques have been developed till date, but efficiency achieved so far leaves room for improvement. This paper also, presents a technique that aims at automatic detection of cardiac abnormality using an Artificial Neural Network. The detection is done on the basis of the wave shapes of different QRS complexes for different arrhythmias which are extracted from the ECG beats using Wavelet Transform. As the Daubechies wavelets are similar in shape to the QRS complex of the ECG, db4 has been used in the above context. The performance accuracies achieved for training, testing known data and unknown data have been found to be 99.7%, 99.2% and 96.2% respectively. The MIT-BIH database has been used for the present study and an altogether of seven different beats have been used for classification

Contributions to improve the technologies supporting unmanned aircraft operations

Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

Score-based Data Assimilation

Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem of identifying plausible state trajectories that explain noisy or incomplete observations of stochastic dynamical systems. Various approaches have been proposed to solve this problem, including particle-based and variational methods. However, most algorithms depend on the transition dynamics for inference, which becomes intractable for long time horizons or for high-dimensional systems with complex dynamics, such as oceans or atmospheres. In this work, we introduce score-based data assimilation for trajectory inference. We learn a score-based generative model of state trajectories based on the key insight that the score of an arbitrarily long trajectory can be decomposed into a series of scores over short segments. After training, inference is carried out using the score model, in a non-autoregressive manner by generating all states simultaneously. Quite distinctively, we decouple the observation model from the training procedure and use it only at inference to guide the generative process, which enables a wide range of zero-shot observation scenarios. We present theoretical and empirical evidence supporting the effectiveness of our method

Shared control for navigation and balance of a dynamically stable robot.

by Law Kwok Ho Cedric.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 106-112).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related work --- p.4Chapter 1.3 --- Thesis overview --- p.5Chapter 2 --- Single wheel robot: Gyrover --- p.9Chapter 2.1 --- Background --- p.9Chapter 2.2 --- Robot concept --- p.11Chapter 2.3 --- System description --- p.14Chapter 2.4 --- Flywheel characteristics --- p.16Chapter 2.5 --- Control patterns --- p.20Chapter 3 --- Learning Control --- p.22Chapter 3.1 --- Motivation --- p.22Chapter 3.2 --- Cascade Neural Network with Kalman filtering --- p.24Chapter 3.3 --- Learning architecture --- p.27Chapter 3.4 --- Input space --- p.29Chapter 3.5 --- Model evaluation --- p.30Chapter 3.6 --- Training procedures --- p.35Chapter 4 --- Control Architecture --- p.38Chapter 4.1 --- Behavior-based approach --- p.38Chapter 4.1.1 --- Concept and applications --- p.39Chapter 4.1.2 --- Levels of competence --- p.44Chapter 4.2 --- Behavior-based control of Gyrover: architecture --- p.45Chapter 4.3 --- Behavior-based control of Gyrover: case studies --- p.50Chapter 4.3.1 --- Vertical balancing --- p.51Chapter 4.3.2 --- Tiltup motion --- p.52Chapter 4.4 --- Discussions --- p.53Chapter 5 --- Implement ation of Learning Control --- p.57Chapter 5.1 --- Validation --- p.57Chapter 5.1.1 --- Vertical balancing --- p.58Chapter 5.1.2 --- Tilt-up motion --- p.62Chapter 5.1.3 --- Discussions --- p.62Chapter 5.2 --- Implementation --- p.65Chapter 5.2.1 --- Vertical balanced motion --- p.65Chapter 5.2.2 --- Tilt-up motion --- p.68Chapter 5.3 --- Combined motion --- p.70Chapter 5.4 --- Discussions --- p.72Chapter 6 --- Shared Control --- p.74Chapter 6.1 --- Concept --- p.74Chapter 6.2 --- Schemes --- p.78Chapter 6.2.1 --- Switch mode --- p.79Chapter 6.2.2 --- Distributed mode --- p.79Chapter 6.2.3 --- Combined mode --- p.80Chapter 6.3 --- Shared control of Gyrover --- p.81Chapter 6.4 --- How to share --- p.83Chapter 6.5 --- Experimental study --- p.88Chapter 6.5.1 --- Heading control --- p.89Chapter 6.5.2 --- Straight path --- p.90Chapter 6.5.3 --- Circular path --- p.91Chapter 6.5.4 --- Point-to-point navigation --- p.94Chapter 6.6 --- Discussions --- p.95Chapter 7 --- Conclusion --- p.103Chapter 7.1 --- Contributions --- p.103Chapter 7.2 --- Future work --- p.10

The modeling of human sensation in virtual environments.

Ka Keung Caramon Lee.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 100-105).Abstracts in English and Chinese.Contents --- p.iiiList of Figures --- p.viList of Tables --- p.ixChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Related Work --- p.3Chapter 1.2.1 --- Empirical Psychophysical Equations --- p.3Chapter 1.2.2 --- Industry Standards --- p.4Chapter 1.2.3 --- Fuzzy Logic --- p.4Chapter 1.2.4 --- Neural Networks --- p.5Chapter 1.3 --- Organization of Thesis --- p.7Chapter 2 --- Experimental Design --- p.9Chapter 2.1 --- Human Motion Sense --- p.9Chapter 2.2 --- Full-Body Motion Virtual Reality System --- p.12Chapter 2.3 --- Human Sensation Measure --- p.15Chapter 2.4 --- Trajectory Segmentation --- p.16Chapter 3 --- Learning and Validation of Human Sensation Models --- p.22Chapter 3.1 --- Cascade Neural Networks --- p.23Chapter 3.1.1 --- Dynamic Mapping --- p.26Chapter 3.2 --- Experimental Trajectory Data --- p.26Chapter 3.3 --- Effect of Trajectory Segmentation --- p.31Chapter 3.4 --- Model Validation --- p.32Chapter 3.5 --- Similarity Measure --- p.33Chapter 3.6 --- Similarity Measure Results --- p.38Chapter 4 --- Input Reduction for Human Sensation Modeling --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Input Reduction --- p.41Chapter 4.3 --- Feature Extraction and Input Selection --- p.42Chapter 4.4 --- Feature Extraction Using Principal Component Analysis --- p.44Chapter 4.5 --- Independent Component Analysis --- p.48Chapter 4.5.1 --- Measure of Gaussianity --- p.50Chapter 4.5.2 --- The Fixed Point ICA Algorithm --- p.51Chapter 4.6 --- Input Reduction Using Independent Component Analysis --- p.52Chapter 4.6.1 --- ICA Without Dimension Reduction --- p.52Chapter 4.6.2 --- Feature Extraction Using ICA --- p.55Chapter 4.6.3 --- Input Selection Using ICA --- p.57Chapter 4.6.4 --- Applying Input Selection by ICA on the Furnace Data --- p.58Chapter 4.6.5 --- Applying Input Selection by ICA to Sensation Modeling --- p.65Chapter 4.6.6 --- Cross Verification of Selected Inputs --- p.70Chapter 4.7 --- Summary on Input Reduction for Human Sensation Modeling --- p.72Chapter 5 --- Stimulus Modification Based on Human Sensation --- p.74Chapter 5.1 --- Need for Stimulus Modification --- p.74Chapter 5.2 --- Sensation Grades --- p.75Chapter 5.3 --- Trajectory Modification Scheme --- p.77Chapter 5.4 --- Experiments --- p.80Chapter 6 --- Conclusion --- p.86Chapter 6.1 --- Contributions --- p.86Chapter 6.2 --- Future Work --- p.87Chapter A --- Platform Model --- p.88Chapter A.1 --- Inverse Kinematics --- p.90Chapter A.2 --- Forward Kinematics --- p.93Chapter A.3 --- Platform Dynamics --- p.99Bibliography --- p.10

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Information Theory and Cooperative Control in Networked Multi-Agent Systems with Applications to Smart Grid

This dissertation focuses on information theoretic aspects of and cooperative control techniques in networked multi-agent systems (NMAS) with communication constraints. In the first part of the dissertation, information theoretic limitations of tracking problems in networked control systems, especially leader-follower systems with communication constraints, are studied. Necessary conditions on the data rate of each communication link for tracking of the leader-follower systems are provided. By considering the forward and feedback channels as one cascade channel, we also provide a lower bound for the data rate of the cascade channel for the system to track a reference signal such that the tracking error has finite second moment. Finally, the aforementioned results are extended to the case in which the leader system and follower system have different system models. In the second part, we propose an easily scalable hierarchical decision-making and control architecture for smart grid with communication constraints in which distributed customers equipped with renewable distributed generation (RDG) interact and trade energy in the grid. We introduce the key components and their interactions in the proposed control architecture and discuss the design of distributed controllers which deal with short-term and long-term grid stability, power load balancing and energy routing. At microgrid level, under the assumption of user cooperation and inter-user communications, we propose a distributed networked control strategy to solve the demand-side management problem in microgrids. Moreover, by considering communication delays between users and microgrid central controller, we propose a distributed networked control strategy with prediction to solve the demand-side management problem with communication delays. In the third part, we consider the disturbance attenuation and stabilization problem in networked control systems. To be specific, we consider the string stability in a large group of interconnected systems over a communication network. Its potential applications could be found in formation tracking control in groups of robots, as well as uncertainty reduction and disturbance attenuation in smart grid. We propose a leader-following consensus protocol for such interconnected systems and derive the sufficient conditions, in terms of communication topology and control parameters, for string stability. Simulation results and performance in terms of disturbance propagation are also given. In the fourth part, we consider distributed tracking and consensus in networked multi-agent systems with noisy time-varying graphs and incomplete data. In particular, a distributed tracking with consensus algorithm is developed for the space-object tracking with a satellite surveillance network. We also intend to investigate the possible application of such methods in smart grid networks. Later, conditions for achieving distributed consensus are discussed and the rate of convergence is quantified for noisy time-varying graphs with incomplete data. We also provide detailed simulation results and performance comparison of the proposed distributed tracking with consensus algorithm in the case of space-object tracking problem and that of distributed local Kalman filtering with centralized fusion and centralized Kalman filter. The information theoretic limitations developed in the first part of this dissertation provide guildlines for design and analysis of tracking problems in networked control systems. The results reveal the mutual interaction and joint application of information theory and control theory in networked control systems. Second, the proposed architectures and approaches enable scalability in smart grid design and allow resource pooling among distributed energy resources (DER) so that the grid stability and optimality is maintained. The proposed distributed networked control strategy with prediction provides an approach for cooperative control at RDG-equipped customers within a self-contained microgrid with different feedback delays. Our string stability analysis in the third part of this dissertation allows a single networked control system to be extended to a large group of interconnected subsystems while system stability is still maintained. It also reveals the disturbance propagation through the network and the effect of disturbance in one subsystem on other subsystems. The proposed leader-following consensus protocol in the constrained communication among users reveals the effect of communication in stabilization of networked control systems and the interaction between communication and control over a network. Finally, the distributed tracking and consensus in networked multi-agent systems problem shows that information sharing among users improves the quality of local estimates and helps avoid conflicting and inefficient distributed decisions. It also reveals the effect of the graph topologies and incomplete node measurements on the speed of achieving distributed decision and final consensus accuracy