Robust longitudinal rate gyro bias estimation for reliable pitch attitude observation through utilization of a displaced accelerometer array

In this thesis, a novel attitude estimation device is proposed utilizing cost-effective measurement sensors. The device fuses a rate gyroscope with an accelerometer array to estimate and eliminate the rate gyro bias online yielding accurate real time aircraft attitude tracking. Attitude determination algorithms are dependent on instantaneous and accurate measurements of translational and rotational body rates for precise estimation of vehicle orientation in three-dimensional space. Measurement error of instantaneous rate sensors, gyroscopes, is introduced via inherent biases and signal noise resulting in gyro drift. Integration of the rate signal for calculation of a net displacement amplifies these minute measurement errors leading to inaccurate and unreliable attitude observation. The proposed device is a departure from typical attitude observers and bias estimators due to its reliance on accelerometers measuring the local gravitational vector in lieu of additional magnetic field sensors or GPS. The end result of this work is a longitudinal attitude estimation device able to compute a rate gyro bias in real-time producing accurate pitch angle tracking while subjected to simulated aircraft flight conditions. The effectiveness of the newly constructed attitude estimation algorithm is demonstrated by comparison of attitude and rate gyro bias estimates produced from noise corrupted and biased sensors with the actual attitude of a nonlinear aircraft model and true rate gyro bias

A Drift Eliminated Attitude & Position Estimation Algorithm In 3D

Inertial wearable sensors constitute a booming industry. They are self contained, low powered and highly miniaturized. They allow for remote or self monitoring of health-related parameters. When used to obtain 3-D position, velocity and orientation information, research has shown that it is possible to draw conclusion about issues such as fall risk, Parkinson disease and gait assessment. A key issues in extracting information from accelerometers and gyroscopes is the fusion of their noisy data to allow accurate assessment of the disease. This, so far, is an unsolved problem. Typically, a Kalman filter or its nonlinear, non-Gaussian version are implemented for estimating attitude â?? which in turn is critical for position estimation. However, sampling rates and large state vectors required make them unacceptable for the limited-capacity batteries of low-cost wearable sensors. The low-computation cost complementary filter has recently been re-emerging as the algorithm for attitude estimation. We employ it with a heuristic drift elimination method that is shown to remove, almost entirely, the drift caused by the gyroscope and hence generate a fairly accurate attitude and drift-eliminated position estimate. Inertial sensor data is obtained from the 10-axis SP-10C sensor, attached to a wearable insole that is inserted in the shoe. Data is obtained from walking in a structured indoor environment in Votey Hall

Widely Linear State Space Filtering of Improper Complex Signals

Complex signals are the backbone of many modern applications, such as power systems, communication systems, biomedical sciences and military technologies. However, standard complex valued signal processing approaches are suited to only a subset of complex signals known as proper, and are inadequate of the generality of complex signals, as they do not fully exploit the available information. This is mainly due to the inherent blindness of the algorithms to the complete second order statistics of the signals, or due to under-modelling of the underlying system. The aim of this thesis is to provide enhanced complex valued, state space based, signal processing solutions for the generality of complex signals and systems. This is achieved based on the recent advances in the so called augmented complex statistics and widely linear modelling, which have brought to light the limitations of conventional statistical complex signal processing approaches. Exploiting these developments, we propose a class of widely linear adaptive state space estimation techniques, which provide a unified framework and enhanced performance for the generality of complex signals, compared with conventional approaches. These include the linear and nonlinear Kalman and particle filters, whereby it is shown that catering for the complete second order information and system models leads to significant performance gains. The proposed techniques are also extended to the case of cooperative distributed estimation, where nodes in a network collaborate locally to estimate signals, under a framework that caters for general complex signals, as well as the cross-correlations between observation noises, unlike earlier solutions. The analysis of the algorithms are supported by numerous case studies, including frequency estimation in three phase power systems, DIFAR sonobuoy underwater target tracking, and real-world wind modeling and prediction.Open Acces

DIMENSIONALITY REDUCTION INCONTROL AND COORDINATION OF HUMAN HAND

The human hand is an excellent example of versatile architecture which can easily accomplish numerous tasks with very least effort possible. Researchers have been trying to analyze the complex architecture of the human hand. It is an unsolved mystery even today how Central Nervous System (CNS) controls the high degree of freedom (DoF) of the human hand. Investigators have put forth numerous theories which support movement planning both at higher and lower levels of the neural system as well as the bio mechanical system. This planning is hypothesized to happen in a reduced dimensionality space of tiny modules of movement called movement primitives often referred to as synergies. These synergies are physiologically significant in planning and control of movement.This dissertation presents time-varying kinematic synergies which linearly combine to generate the entire movement. The decomposition of these synergies becomes an exciting optimization problem and even more fascinating as it addresses two most important problems of motor control—coordination and dimensionality reduction. In this dissertation, a new model of convolutive mixtures for generation of joint movements is proposed. According to this model, an impulse originated in the higher-level neural system evokes the activation of some circuits in the lower-level neural system, then stimulates certain biomechanical structures, and eventually creates a stereotyped angular change at each finger-joint of the hand. Current model enabled greater access to existing blind source separation algorithms which reduce the computational complexity. First, kinematic synergies were extracted from a well known matrix factorization method, namely principal component analysis. By using the above kinematic synergies, a method to obtain temporal postural synergies is established. These temporal postural synergies were further used in the model of convolutive mixtures. An optimal selection of these temporal synergies which can reconstruct movements is then achieved by l1-minimization. The realization of the model by l1-minimization out performed the previous models which use steepest descent gradient methods. Synergies have received increased attention in the fields of robotics, human computer interface, telesurgery and rehabilitation. Improved performance and new computational model to decompose synergies presented here might enable them to be appropriate for real time applications

Gaussian Conditionally Markov Sequences: Theory with Application

Markov processes have been widely studied and used for modeling problems. A Markov process has two main components (i.e., an evolution law and an initial distribution). Markov processes are not suitable for modeling some problems, for example, the problem of predicting a trajectory with a known destination. Such a problem has three main components: an origin, an evolution law, and a destination. The conditionally Markov (CM) process is a powerful mathematical tool for generalizing the Markov process. One class of CM processes, called $CM_L$, fits the above components of trajectories with a destination. The CM process combines the Markov property and conditioning. The CM process has various classes that are more general and powerful than the Markov process, are useful for modeling various problems, and possess many Markov-like attractive properties. Reciprocal processes were introduced in connection to a problem in quantum mechanics and have been studied for years. But the existing viewpoint for studying reciprocal processes is not revealing and may lead to complicated results which are not necessarily easy to apply. We define and study various classes of Gaussian CM sequences, obtain their models and characterizations, study their relationships, demonstrate their applications, and provide general guidelines for applying Gaussian CM sequences. We develop various results about Gaussian CM sequences to provide a foundation and tools for general application of Gaussian CM sequences including trajectory modeling and prediction. We initiate the CM viewpoint to study reciprocal processes, demonstrate its significance, obtain simple and easy to apply results for Gaussian reciprocal sequences, and recommend studying reciprocal processes from the CM viewpoint. For example, we present a relationship between CM and reciprocal processes that provides a foundation for studying reciprocal processes from the CM viewpoint. Then, we obtain a model for nonsingular Gaussian reciprocal sequences with white dynamic noise, which is easy to apply. Also, this model is extended to the case of singular sequences and its application is demonstrated. A model for singular sequences has not been possible for years based on the existing viewpoint for studying reciprocal processes. This demonstrates the significance of studying reciprocal processes from the CM viewpoint

Design and optimization of wireless sensor networks for localization and tracking

Knowledge of the position of nodes in a WSN is crucial in most wireless sensor network (WSN) applications. The gathered information needs to be associated with a particular location in a specific time instant in order to appropiately control de surveillance area. Moreover, WSNs may be used for tracking certain objects in monitoring applications, which also requires the incorporation of location information of the sensor nodes into the tracking algorithms. These requisites make localizacion and tracking two of the most important tasks of WSN. Despite of the large research efforts that have been made in this field, considerable technical challenges continue existing in subjects areas like data processing or communications. This thesis is mainly concerned with some of these technical problems. Specifically, we study three different challenges: sensor deployment, model independent localization and sensor selection. The first part of the work is focused on the task of sensor deployement. This is considered critical since it affects cost, detection, and localization accuracy of a WSN. There have been significant research efforts on deploying sensors from different points of view, e.g. connectivity or target detection. However, in the context of target localization, we believe it is more convenient to deploy the sensors in views of obtaining the best estimation possible on the target positioning. Therefore, in this work we suggest an analysis of the deployment from the standpoint of the error in the position estimation. To this end, we suggest the application of the modified Cram´er-Rao bound (MCRB) in a sensor network to perform a prior analysis of the system operation in the localization task. This analysis provides knowledge about the system behavior without a complete deployment. It also provides essential information to select fundamental parameters properly, like the number of sensors. To do so, a complete formulation of the modified information matrix (MFIM) and MCRB is developed for the most common measurement models, such as received signal strength (RSS), time-of-arrival (ToA) and angle-of-arrival (AoA). In addition, this formulation is extended for heterogeneous models that combine different measurement models. Simulation results demonstrate the utility of the proposed analysis and point out the similarity between MCRB and CRB. Secondly, we address the problem of target localization which encompasses many of the challenging issues which commonly arise in WSN. Consequently, many localization algorithms have been proposed in the literature each one oriented towards solving these issues. Nevertheless, it have seen tahta the localization performance of above methods usually relies heavily on the availability of accurate knowledge regarding the observation model. When errors in the measurement model are present, their target localization accuracy is degraded significantly. To overcome this problem, we proposed a novel localization algorithm to be used in applications where the measurement model is not accurate or incomplete. The independence of the algorithm from the model provides robustness and versatility. In order to do so, we apply radial basis functions (RBFs) interpolation to evaluate the measurement function in the entire surveillance area, and estimate the target position. In addition, we also propose the application of LASSO regression to compute the weigths of the RBFs and improve the generalization of the interpolated function. Simulation results have demonstrated the good performance of the proposed algorithm in the localization of single or multiples targets. Finally, we study the sensor selection problem. In order to prolong the network lifetime, sensors alternate their state between active and idle. The decision of which sensor should be activated is based on a variety of factors depending on the algorithm or the sensor application. Therefore, here we investigate the centralized selection of sensors in target-tracking applications over huge networks where a large number of randomly placed sensors are available for taking measurements. Specifically, we focus on the application of optimization algorithms for the selection of sensors using a variant of the CRB, the Posterior CRB (PCRB), as the performance-based optimization criteria. This bound provides the performance limit on the mean square error (MSE) for any unbiased estimator of a random parameter, and is iteratively computed by a particle filter (in our case, by a Rao-Blackwellized Particle Filter). In this work we analyze, and compare, three optimization algorithms: a genetic algorithm (GA), the particle swarm optimization (PSO), and a new discrete-variant of the cuckoo search (CS) algorithm. In addition, we propose a local-search versions of the previous optimization algorithms that provide a significant reduction of the computation time. Lastly, simulation results demonstrate the utility of these optmization algorithm to solve a sensor selection problem and point out the reduction of the computation time when local search is applied. ---------------------------------------------------Las redes de sensores se presentan como una tecnología muy interesante que ha atraído considerable interés por parte de los investigadores en la actualidad [1, 109]. Recientes avances en electrónica y en comunicaciones inalámbricas han permitido de desarrollo de sensores de bajo coste, baja potencia y multiples funciones, de reducido tamaño y con capacidades de comunicación a cortas distancias. Estos sensores, desplegados en gran número y unidos a través de comunicaciones inalámbricas, proporcionan grandes oportunidades en aplicaciones como la monitorización y el control de casas, ciudades o el medio ambiente. Un nodo sensor es un dispositivo de baja potencia capaz de interactuar con el medio a través de sus sensores, procesar información localmente y comunicar dicha información a tus vecinos más próximos. En el mercado existe una gran variedad de sensores (magnéticos, acústicos, térmicos, etc), lo que permite monitorizar muy diversas condiciones ambientales (temperatura, humedad, etc.) [25]. En consecuencia, las redes de sensores presentan un amplio rango de aplicaciones: seguridad en el hogar, monitorización del medio, análisis y predicción de condiciones climáticas, biomedicina [79], etc. A diferencia de las redes convencionales, las redes de sensores sus propias limitaciones, como la cantidad de energía disponible, el corto alcance de sus comunicaciones, su bajo ancho de band y sus limitaciones en el procesado de información y el almacenamiento de la misma. Por otro parte, existen limitaciones en el diseño que dependerán directamente de la aplicación que se le quiera dar a la red, como por ejemplo el tamaño de la red, el esquema de despliegue o la topología de la red..........Presidente: Jesús Cid Sueiro; Vocal: Mónica F. Bugallo; Secretario: Sancho Salcedo San

Techniques for effective virtual sensor development and implementation with application to air data systems

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen716. INGEGNERIA AEROSPAZIALEnoopenBrandl, Albert