Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints

This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical characteristics. When considering the limited resource problems of two kinds of communication channels (i.e., sensor-to-remote estimator channel and smart sensor-to-fusion center channel), an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified networked framework to lighten the communication burden. Then, two kinds of compensation strategies in terms of a unified model are designed to restructure the untransmitted information, and the local/fusion estimators are proposed based on the compensation information. Furthermore, the linearization errors caused by the Taylor expansion are modeled by the state-dependent matrices with uncertain parameters when establishing estimation error systems, and then different robust recursive optimization problems are constructed to determine the estimator gains and the fusion criteria. Meanwhile, the stability conditions are also proposed such that the square errors of the designed nonlinear estimators are bounded. Finally, a vehicle localization system is employed to demonstrate the effectiveness and advantages of the proposed methods.Comment: 15 pages,9 figures. The first draft was completed in June 2021, and this is the revised versio

Event-based State Estimation in Multisensor Systems

Nowadays sensors are implemented in countless of actual scenarios ranging from securityto entertainment applications. They generate a huge amount of transmissionswithin the network they belong to, resulting in a costly communication e↵ort. Inorder to optimize the transmission process, an event-based system – instead of theconventional periodic approach – should be used. In this sense, several challengesappear when multiple sensors are involved at the same time, where new issues abouttheir event-criteria arise, i.e., how could sensors compare their observations to makea transmission decision. To this e↵ect, communication between sensor nodes is tobe studied seeking to utilize information in a profitable way. In this work, di↵erentmultisensor network structures are to be compared, i.e., star, chain, and hierarchicaltopologies. Finally, quality will be deeply discussed in terms of estimation’s qualitydegradation due to the proposed joint trigger criteria as compared to independentevent triggers.<br /

Automatic Flight Control Systems

The history of flight control is inseparably linked to the history of aviation itself. Since the early days, the concept of automatic flight control systems has evolved from mechanical control systems to highly advanced automatic fly-by-wire flight control systems which can be found nowadays in military jets and civil airliners. Even today, many research efforts are made for the further development of these flight control systems in various aspects. Recent new developments in this field focus on a wealth of different aspects. This book focuses on a selection of key research areas, such as inertial navigation, control of unmanned aircraft and helicopters, trajectory control of an unmanned space re-entry vehicle, aeroservoelastic control, adaptive flight control, and fault tolerant flight control. This book consists of two major sections. The first section focuses on a literature review and some recent theoretical developments in flight control systems. The second section discusses some concepts of adaptive and fault-tolerant flight control systems. Each technique discussed in this book is illustrated by a relevant example

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems

Damage localization in data-driven vibration-based structural health monitoring using linear quadratic estimation theory

Vibration-based Structural Health Monitoring (SHM) is classically approached from two different directions; both involve the acquisition and processing of vibration signals. The first and most popular strategy, which is also followed in the present thesis, relies entirely on the measurements. In contrast, the second approach employs physical models such as finite element (FE) models that are designed based on mechanical principles. In times in which the real-time processing of digital twins for engineering structures becomes more and more realistic, model-based approaches for vibration-based SHM receive increasing attention. Data-driven strategies are still primarily used in vibration-based SHM, and they will remain appealing in situations where precise physical modeling appears cumbersome. Hence, the need for efficient, robust, and reliable data-driven techniques concerning all stages and hurdles of SHM that can prove themselves in practice will never vanish. In this regard, after over 25 years of research, the number of real-life validation studies is still surprisingly low. As for all SHM strategies, the difficulty concerning damage analysis increases with higher levels of realization. Beginning with the goal of detecting damage, SHM finally seeks to predict the remaining lifetime of a structure. The intermediate steps comprise the localization, classification, and assessment of damage. Without the existence of adequately calibrated physics-based models, the successful implementation of methods tackling the objectives beyond damage localization in an unsupervised data-driven scheme is questionable. The term ‘unsupervised’ refers to the fact that knowledge about the manifestation of damage is not available. Especially in civil engineering, this situation pertains in general and is considered in the present thesis. In data-driven SHM, where the area of structural alterations is narrowed down to adjacent sensors, damage localization suffers from the coarse spatial resolution of parsimonious data acquisition systems. Classical modal approaches that hold potential for damage localization require a dense sensor network or significant damage. Originating from the field of fault detection and isolation, estimator- and filter-based methods have proven to be applicable for damage identification of mechanical and civil engineering structures. Notably, they feature an enormous sensitivity towards structural changes when properly designed. Although it remains advantageous for the sake of precise damage localization, these tools such as Kalman or H-infinity filters do not exhibit the inherent demand for a dense sensor network. Consequently, they promise to be viable techniques for the application in vibration-based SHM. A central challenge of this discipline is the discrimination between the natural variability of the structure’s dynamics and the one caused by damage. The former results from varying environmental and operational conditions (EOCs). Especially highly sensitive methods for damage identification are affected by these natural changes, and thus, rely on an efficient data normalization strategy, which can prove itself in practice. In light of these challenges, this thesis provides a real-life validation for the application of quadratic estimators in data-driven vibration-based SHM. To this end, an elaborate technique for estimator-based damage localization is adapted and included in an SHM framework comprising the necessary steps of data normalization and statistical testing. The damage analysis methodology was originally designed for H-infinity filters, which seem well-suited for use in SHM, as they do not assume specific properties of the excitation acting on the structure nor of the involved disturbances. However, previous studies have shown that, in some cases, the filter performance required to achieve high levels of sensitivity towards localized damage cannot be obtained. This issue can be circumvented by employing well-tuned Kalman filters. Therefore, a novel approach for noise covariance estimation is established at first. The associated estimation scheme constitutes a parametric extension of the popular autocovariance least-squares (ALS) technique. The effectiveness of this estimation technique in the context of Kalman filter-based damage localization is studied first using simulations and laboratory experiments. The second part is dedicated to the problem of handling EOCs. This body of work proposes an identification scheme for linear parameter-varying systems based on the interpolation of linear time-invariant systems for different operating points. A simulation study demonstrates the applicability for the purpose of data normalization. Finally, real-life validation of the proposed methods for SHM is conducted. Therefore, a steel lattice mast located outdoors functions as the test object. It is naturally affected by ambient sources of excitation, variability, and uncertainty. The mast, explicitly designed for this validation purpose, is equipped with reversible damage mechanisms that may be activated or removed to reduce the stiffness at multiple locations of the structure. The investigations conducted in this part of the thesis demonstrate proper damage detection of all considered damages as well as localization for the highest degree of severity. These promising results suggest the applicability of the presented methods for Kalman filter tuning, damage localization, and data-normalization in the context of vibration-based SHM

Hillslope-scale soil moisture estimation with a physically-based ecohydrology model and L-band microwave remote sensing observations from space

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.Includes bibliographical references (p. 469-488).Soil moisture is a critical hydrosphere state variable that links the global water, energy, and carbon cycles. Knowledge of soil moisture at scales of individual hillslopes (10's to 100's of meters) is critical to advancing applications such as landslide prediction, rainfall-runoff modeling, and wildland fire fuel load assessment. This thesis develops a data assimilation framework that employs the ensemble Kalman Filter (EnKF) to estimate the spatial distribution of soil moisture at hillslope scales by combining uncertain model estimates with noisy active and passive L-band microwave observations. Uncertainty in the modeled soil moisture state is estimated through Monte Carlo simulations with an existing spatially distributed ecohydrology model. Application of the EnKF to estimate hillslope-scale soil moisture in a watershed critically depends on: (1) identification of factors contributing to uncertainty in soil moisture, (2) adequate representation of the sources of uncertainty in soil moisture, and (3) formulation of an observing system to estimate the geophysically observable quantities based on the modeled soil moisture. Uncertainty in the modeled soil moisture distribution arises principally from uncertainty in the hydrometeorological forcings and imperfect knowledge of the soil parameters required as input to the model. Three stochastic models are used in combination to simulate uncertain hourly hydrometeorological forcings for the model. Soil parameter sets are generated using a stochastic approach that samples low probability but potentially high consequence parameter values and preserves correlation among the parameters. The observing system recognizes the role of the model in organizing the factors effecting emission and reflection of L-band microwave energy and emphasizes the role of topography in determining the satellite viewing geometry at hillslope scales.(cont.) Experiments in which true soil moisture conditions were simulated by the model and used to produce synthetic observations at spatial scales significantly coarser than the model resolution reveal that sequential assimilation of observations improves the hillslope-scale near-surface moisture estimate. Results suggest that the data assimilation framework is an effective means of disaggregating coarse-scale observations according to the model physics represented by the ecohydrology model. The thesis concludes with a discussion of contributions, implications, and future directions of this work.by Alejandro Nicolas Flores.Ph.D