Orbit determination of space objects based on sparse optical data

While building up a catalog of Earth orbiting objects, if the available optical observations are sparse, not deliberate follow ups of specific objects, no orbit determination is possible without previous correlation of observations obtained at different times. This correlation step is the most computationally intensive, and becomes more and more difficult as the number of objects to be discovered increases. In this paper we tested two different algorithms (and the related prototype software) recently developed to solve the correlation problem for objects in geostationary orbit (GEO), including the accurate orbit determination by full least squares solutions with all six orbital elements. Because of the presence in the GEO region of a significant subpopulation of high area to mass objects, strongly affected by non-gravitational perturbations, it was actually necessary to solve also for dynamical parameters describing these effects, that is to fit between 6 and 8 free parameters for each orbit. The validation was based upon a set of real data, acquired from the ESA Space Debris Telescope (ESASDT) at the Teide observatory (Canary Islands). We proved that it is possible to assemble a set of sparse observations into a set of objects with orbits, starting from a sparse time distribution of observations, which would be compatible with a survey capable of covering the region of interest in the sky just once per night. This could result in a significant reduction of the requirements for a future telescope network, with respect to what would have been required with the previously known algorithm for correlation and orbit determination.Comment: 20 pages, 8 figure

Homogenized model for herringbone bond masonry: linear elastic and limit analysis

A kinematic procedure to obtain in-plane elastic moduli and macroscopic masonry strength domains in the case of herringbone masonry is presented. The model is constituted by two central bricks interacting with their neighbors by means of either elastic or rigidplastic interfaces with friction, representing mortar joints. A sub-class of possible elementary deformations is a-priori chosen to describe joints cracking under in- plane loads. Suitable internal macroscopic actions are applied on the Representative Element of Volume REV and the power expended within the 3D bricks assemblage is equated to that expended in the macroscopic 2D Cauchy continuum. The elastic and limit analysis problem at a cell level are solved by means of a quadratic and linear programming approach, respectively. When dealing with the limit analysis approach, several computations are performed investigating the role played by (1) the direction of the load with respect to herringbone bond pattern inclination and (2) masonry textur

Innovative observing strategy and orbit determination for Low Earth Orbit Space Debris

We present the results of a large scale simulation, reproducing the behavior of a data center for the build-up and maintenance of a complete catalog of space debris in the upper part of the low Earth orbits region (LEO). The purpose is to determine the performances of a network of advanced optical sensors, through the use of the newest orbit determination algorithms developed by the Department of Mathematics of Pisa (DM). Such a network has been proposed to ESA in the Space Situational Awareness (SSA) framework by Carlo Gavazzi Space SpA (CGS), Istituto Nazionale di Astrofisica (INAF), DM, and Istituto di Scienza e Tecnologie dell'Informazione (ISTI-CNR). The conclusion is that it is possible to use a network of optical sensors to build up a catalog containing more than 98% of the objects with perigee height between 1100 and 2000 km, which would be observable by a reference radar system selected as comparison. It is also possible to maintain such a catalog within the accuracy requirements motivated by collision avoidance, and to detect catastrophic fragmentation events. However, such results depend upon specific assumptions on the sensor and on the software technologies

The Evolution of the Fed's Inflation Target in an Estimated Model under RE and Learning

This paper aims to infer the evolving Fed's inflation target by estimating a monetary model under the assumptions of RE and learning. The results emphasize how different assumptions about expectations may have important effects on the inferred target movements.Time-varying inflation target; Learning, Expectations, Bayesian estimation

Has globalization transformed U.S. macroeconomic dynamics?

This paper estimates a structural New Keynesian model to test whether globalization has changed the behavior of U.S. macroeconomic variables. Several key coefficients in the model--such as the slopes of the Phillips and IS curves, the sensitivities of domestic inflation and output to "global" output, and so forth--are allowed in the estimation to depend on the extent of globalization (modeled as the changing degree of openness to trade of the economy), and, therefore, they become time-varying. The empirical results indicate that globalization can explain only a small part of the reduction in the slope of the Phillips curve. The sensitivity of U.S. inflation to global measures of output may have increased over the sample, but it remains very small. The changes in the IS curve caused by globalization are similarly modest. Globalization does not seem to have led to an attenuation in the effects of monetary policy shocks. The nested closed economy specification still appears to provide a substantially better fit of U.S. data than various open economy specifications with time-varying degrees of openness. Some time variation in the model coefficients over the postwar sample exists, particularly in the volatilities of the shocks, but it is unlikely to be related to globalization.Globalization ; Macroeconomics - Econometric models ; Inflation (Finance) ; Monetary policy ; Banks and banking, Central ; Phillips curve

Global Slack and Domestic Inflation Rates: A Structural Investigation for G-7 Countries

Recent papers have argued that one implication of globalization is that domestic inflation rates may have now become more a function of ``global", rather than domestic, economic conditions, as postulated by closed-economy Phillips curves. This paper aims to assess the empirical importance of global output in determining domestic inflation rates by estimating a structural model for a sample of G-7 economies. The model can capture the potential effects of global output fluctuations on both the aggregate supply and the aggregate demand relations in the economy and it is estimated using full-information Bayesian methods. The empirical results reveal a significant effect of global output on aggregate demand in most countries. Through this channel, global economic conditions can indirectly affect inflation. The results, instead, do not seem to provide evidence in favor of altering domestic Phillips curves to include global slack as an additional driving variable for inflation.Globalization; Global Slack; Inflation Dynamics; Phillips Curve; Bayesian Estimation

Expectations, Learning, and the Changing Relationship between Oil Prices and the Macroeconomy

This paper estimates a structural general equilibrium model to investigate the changing relationship between the oil price and macroeconomic variables. The oil price, through the role of oil in production and consumption, affects aggregate demand and supply in the model. The assumption of rational expectations is relaxed in favor of learning. Oil prices, therefore, affect the economy through an additional channel, i.e. through their effect on the formation of agents' beliefs. The estimated learning dynamics indicates that economic agents' perceptions about the effects of oil prices on the economy have changed over time: oil prices were perceived to have large effects on output and inflation in the 1970s, but only milder effects after the mid-1980s. Since expectations play a large role in the determination of output and inflation, the effects of oil price increases on expectations can magnify the response of macroeconomic variables to oil price shocks. In the estimated model, in fact, the implied responses of output and inflation to oil price shocks were much more pronounced in the 1970s than in 2008. Therefore, through the time variation in the impact of oil prices on beliefs, the paper can successfully explain the observed weakening of the effects of oil price shocks on real activity and inflation.Oil price; Inflation expectations; Learning; Monetary policy, Effect of energy shocks; Bayesian estimation

Has Globalization Transformed U.S. Macroeconomic Dynamics?

This paper estimates a structural New Keynesian model to test whether globalization has changed the behavior of U.S. macroeconomic variables. Several key coefficients in the model - such as the slopes of the Phillips and IS curves, the sensitivities of domestic inflation and output to "global" output, and so forth - are allowed in the estimation to depend on the extent of globalization (modeled as the changing degree of openness to trade of the economy), and, therefore, they become time-varying. The empirical results indicate that globalization can explain only a small part of the reduction in the slope of the Phillips curve. The sensitivity of U.S. inflation to global measures of output may have increased over the sample, but it remains very small. The changes in the IS curve caused by globalization are similarly modest. Globalization does not seem to have led to an attenuation in the effects of monetary policy shocks. The nested closed economy specification still appears to provide a substantially better fit of U.S. data than various open economy specifications with time-varying degrees of openness. Some time variation in the model coefficients over the post-war sample exists, particularly in the volatilities of the shocks, but it is unlikely to be related to globalization.Globalization and Inflation; Global slack; Openness; New Keynesian model; Expectations and adaptive learning; DSGE model with time-varying coefficients