Unemployment and Hysteresis: A Nonlinear Unobserved Components A Nonlinear Unobserved Components A Nonlinear Unobserved Components A Nonlinear Unobserved Components A Nonlinear Unobserved Components Approach

A new test for hysteresis based on a nonlinear unobserved components model is proposed. Observed unemployment rates are decomposed into a natural rate component and a cyclical component. Threshold type nonlinearities are introduced by allowing past cyclical unemployment to have a different impact on the natural rate depending onthe regime of the economy. The impact of lagged cyclical shocks on thecurrent natural component is the measure of hysteresis. To derive anappropriate p-value for a test for hysteresis two alternative bootstrapalgorithms are proposed: the first is valid under homoskedastic errorsand the second allows for heteroskedasticity of unknown form. A MonteCarlo simulation study shows the good performance of both bootstrapalgorithms. The bootstrap testing procedure is applied to data fromItaly, France and the United States. We find evidence of hysteresis forall countries under study.Hysteresis, Unobserved Components Model, Threshold Autoregressive Models, Nuisance parameters, Bootstrap

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

Structural Time Series Models and the Kalman Filter: a concise review

The continued increase in availability of economic data in recent years and, more impor- tantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci?cations we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman ?lter algorithm is described taking into account its di¤erent stages, from initialisation to parameter?s estimation. JEL codes: C10, C22, C32

Modern control concepts in hydrology

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise

An analytical investigation of acquisition techniques and system integration studies for a radar aircraft guidance research facility, phase 2

A review of user requirements and updated instrumentation plans are presented for the aircraft tracking and guidance facility at NASA Wallops Station. User demand has increased as a result of new flight research programs; however, basic requirements remain the same as originally reported. Instrumentation plans remain essentially the same but with plans for up- and down-link telemetry more firm. With slippages in the laser acquisition schedule, added importance is placed on the FPS-16 radar as the primary tracking device until the laser is available. Limited simulation studies of a particular Kalman-type filter are also presented. These studies simulated the use of the filter in a helicopter guidance loop in a real-time mode. Disadvantages and limitations of this mode of operation are pointed out. Laser eyesafety calculations show that laser tracking of aircraft is readily feasible from the eyesafety viewpoint

Diagnostic checking and intra-daily effects in time series models

A variety of topics on the statistical analysis of time series are addressed in this thesis. The main emphasis is on the state space methodology and, in particular, on structural time series (STS) models. There are now many applications of STS models in the literature and they have proved to be very successful. The keywords of this thesis vary from - Kalman filter, smoothing and diagnostic checking - to - time-varying cubic splines and intra-daily effects -. Five separate studies are carried out for this research project and they are reflected in the chapters 2 to 6. All studies concern time series models which are placed in the state space form (SSF) so that the Kalman filter (KF) can be applied for estimation. The SSF and the KF play a central role in time series analysis that can be compared with the important role of the regression model and the method of least squares estimation in econometrics. Chapter 2 gives an overview of the latest developments in the state space methodology including diffuse likelihood evaluation, stable calculations, etc. Smoothing algorithms evaluate the full sample estimates of unobserved components in time series models. New smoothing algorithms are developed for the state and the disturbance vector of the SSF which are computationally efficient and outperform existing methods. Chapter 3 discusses the existing and the new smoothing algorithms with an emphasis on theory, algorithms and practical implications. The new smoothing results pave the way to use auxiliary residuals, that is full sample estimates of the disturbances, for diagnostic checking of unobserved components time series models. Chapter 4 develops test statistics for auxiliary residuals and it presents applications showing how they can be used to detect and distinguish between outliers and structural change. A cubic spline is a polynomial function of order three which is regularly used for interpolation and curve-fitting. It has also been applied to piecewise regressions, density approximations, etc. Chapter 5 develops the cubic spline further by allowing it to vary over time and by introducing it into time series models. These timevarying cubic splines are an efficient way of handling slowly changing periodic movements in time series. This method for modelling a changing periodic pattern is applied in a structural time series model used to forecast hourly electricity load demand, with the periodic movements being intradaily or intra-weekly. The full model contains other components, including a temperature response which is also modelled using cubic splines. A statistical computer package (SHELF) is developed to produce, at any time, hourly load forecasts three days ahead

Improved direct torque control using Kalman filter: application to a doubly-fed machine

Direct Torque Control (DTC) has been extensively researched and applied during the last two decades. However, it has only first been applied to the Brushless Doubly Fed Reluctance Machine (BDFRM) a few years ago in its basic form inheriting its intrinsic flux estimation problems that propagate throughout the algorithm and hence compromise the DTC performance. In this paper, we propose the use of Kalman Filter (KF) as an alternative to improve the estimation and consequently the control performance of the DTC. The KF is designed around a nominal model, but is shown to be reliable over the whole operating range of the BDFRM. Moreover, we use a modified robust exact differentiator based on Sliding Mode (SM) techniques to calculate the angular velocity from an angular position encoder. Computer simulations are meticulously designed to take into account real-world physical constraints and thus show illustrative supporting results as expected from an experimental setup

UAV perception for safe flight under physical interaction

Aplicat embargament des de la data de defensa fins al maig 2020The control of autonomous flying vehicles with navigation purposes is a challenging task. Complexity arises mainly due to the non-linearity and uncertainty inherently present in the flight mechanics and aircraft-air interactions. Recently, interest has grown for equipping unmanned vehicles with the capacity to interact with their environment, other vehicles or humans. This will enable interesting applications such as autonomous load carrying, aerial refueling or parcel delivering. Having measured the interaction wrenches ease the control problem which can be configured to reject disturbances or to take profit of them to fulfill mission objectives. This thesis will contribute to this area by providing perception solutions which use limited and low cost sensors that enable state and disturbance estimation for possible, but not restricted to, interaction scenarios. This thesis contain three parts. The first part, introduces basic concepts related to the navigation state, aircraft dynamics, and sensor models. In addition, the platform under study is presented and mathematical models associated to it are calibrated. The second part is devoted to the observability analysis and the design of state observers. Linear and non-linear observability analysis techniques are used to unveil that the state of quadrotors equipped with GPS, magnetometers an IMU sensors cannot be uniquely identified in some specific flight configurations. Results of this section are relevant because the conflicting flight configurations contain hover, a flight maneuverer central in many unmanned aerial missions of VtoL vehicles. For many possible singular configurations, insightful descriptions and interpretations of the solution space known as indistinguishable region is provided. Findings are verified in simulation scenarios where it can be seen how a filter fails to recover the true state of an aircraft when imposing the hover flight condition. We discuss then the design of Extended Kalman Filters for state estimation that considers the available sensors. Issues that are typically not reported in the literature, such as when to update or propagate in the estimator algorithm or which coordinate frame should be used to represent each state variable are discussed. This leads to the formulation of four potentially equivalent but different discrete event-based filters for which precise algorithmic expressions are given. We compare the results of the four filters in simulation under known favorable conditions for observability. In order to diminish the effect of flying in the conflicting observability configurations, we provide an alternative filter based on the Schmidt Kalman Filter (SKF). The proposed filter shares the structure of the EKF, behaves better in the instants that the EKF fails and provides similar results in the remaining conditions. The last part of the thesis deals with the estimation of external disturbances. Disturbance estimation results are based on the derivation of a linear model for the aircraft dynamics which then extended with a high order disturbance model to enable the estimation of fast varying disturbances. Two external disturbance estimators from the literature are reviewed and adapted to the new model. Also, two Kalman observers that exploit the linearity of the derived model are presented. A simulation comparison is provided demonstrating that the KF disturbance estimators outperform the other. In addition, this part presents a design methodology of generic quadratic bounded observers for linear systems with ellipsoidal bounded uncertainty. The derived observers maximize a user tunable compromise between the estimation convergence speed and the final volume containing the estimation error. An observer for disturbances acting on a flying platform is derived considering the high order disturbance model above mentioned. Finally, an analysis of the estimation performance with respect to the design parameters is presented.Esta tesis, contribuye en este área formulando soluciones de percepción que permiten la estimación del estado y perturbaciones externas en condiciones normales de vuelos así como casos de interacción para UAVs equipados con sensores limitados y de bajo coste. La tesis se estructura en tres partes. La primera de ellas introduce los conceptos básicos relacionados con el estado de navegación, la dinámica de la aeronave y modelos de sensores. Además, se presenta la plataforma de estudio así como los modelos matemáticos asociados a ella y su calibración. La segunda parte está destinada al análisis de observabilidad y el diseño de observadores de estado. Los resultados de esta sección son importantes porque dentro de las condiciones de vuelo conflictivas se encuentra el vuelo a punto fijo, una maniobra de vuelo central en muchas misiones de vehículos VToL. Se analizan estas condiciones críticas de vuelo y para ellas se deriva y describe el espacio de soluciones posible conocido como región indistinguible. Los resultados son verificados en simulación dónde se puede apreciar como un estimador de estado falla al intentar realizar su tarea cuando la aeronave está en vuelo a punto fijo. Seguidamente se presenta el diseño de filtros extendidos de Kalman (EKF) que proveen estimaciones del estado con la información limitada de los sensores disponibles. Se discuten conceptos que habitualmente no se presentan en la literatura como cuando actualizar o propagar en el algoritmo de estimación o que sistema de referencia se debe utilizar para representar adecuadamente las variables de estado. Esto lleva a la formulación algorítmica de cuatro filtros discretos basados en eventos, diferentes, pero en esencia equivalentes. Se derivan rutinas de inicialización para los filtros y se comparan los resultados en simulación bajo condiciones favorables de estimación. Con la idea de disminuir el efecto de volar en configuraciones de observabilidad conflictivas, se deriva un filtro alternativo basado en el filtro de Schmidt Kalman (SKF). El filtro propuesto comparte estructura con el EKF, tiene un mejor comportamiento allí dónde le EKF falla y una respuesta similar en el resto de condiciones de vuelo. La última parte de la tesis trata con la estimación de perturbaciones externas. Para ello se deriva un modelo lineal que relaciona fuerzas y momentos con velocidades junto a un modelo de alto orden para las perturbaciones. Se estudia su aplicación a dos modelos para la estimación de perturbaciones ya presentes en la literatura. Además, se proponen dos nuevos filtros de Kalman que se aprovechan de la linealidad del modelo. Se presenta una comparativa basada en la simulación de escenarios ideales así como realistas que demuestra que los filtros KF superan al resto. Esta misma parte de la tesis presenta el diseño genérico de estimadores "quadratic bounded" para sistemas dinámicos lineales cuya incertidumbre se encuentra acotada dentro de elipsoides. Estos estimadores maximizan un compromiso, ajustable por el usuario que contempla la velocidad de convergencia así como el volumen de la solución final que contiene el error de estimación. Se deriva un observador de perturbaciones para plataformas aéreas basado en el modelo de alto orden arriba mencionado. Finalmente, se presenta un análisis del desempeño de estimación en función de los parámetros de diseño del filtro.Postprint (published version