THE CLUSTER ION SPECTROMETRY (CIS) EXPERIMENT

The Cluster Ion Spectrometry (CIS) experiment is a comprehensive ionic plasma spectrometry package on-board the four Cluster spacecraft capable of obtaining full three-dimensional ion distributions with good time resolution (one spacecraft spin) with mass per charge composition determination. The requirements to cover the scientific objectives cannot be met with a single instrument. The CIS package therefore consists of two different instruments, a Hot Ion Analyser (HIA) and a time-of-flight ion COmposition and DIstribution Function analyser (CODIF), plus a sophisticated dual-processor-based instrument-control and Data-Processing System (DPS), which permits extensive on-board data-processing. Both analysers use symmetric optics resulting in continuous, uniform, and well-characterised phase space coverage. CODIF measures the distributions of the major ions (H⁺, He⁺, He⁺⁺, and O⁺) with energies from ~0 to 40 keV/e with medium (22.5°) angular resolution and two different sensitivities. HIA does not offer mass resolution but, also having two different sensitivities, increases the dynamic range, and has an angular resolution capability (5.6° × 5.6°) adequate for ion-beam and solar-wind measurements.     

Control of a large space structure using MMAE/MMAC techniques

The application of moving-bank multiple model adaptive estimation and control (MMAE/MMAC) algorithms to an actual spade structure (Space Integrated Controls Experiment (SPICE)) being examined at Phillips Laboratory at Kirtland AFB, NM, is presented. The structure consists of a large platform and a smaller platform connected by three legs in a tripod fashion. Kalman filtering and LQG (linear system, quadratic cost, Gaussian noise) control techniques are utilized as the primary design tools for the components of the MMAE/MMAC. Implementing a bank of filters or controllers increases the robustness of the algorithms when uncertainties exist in the system model, whereas the moving bank is utilized to reduce the computational load. Several reduced-order models are developed from the truth model using modal analysis and modal cost analysis. The MMAE/MMAC design with a substantially reduced-order filter model provides an excellent method to estimate a wide range of parameter variations and to quell oscillations in the structure.     

Performance limits for processor networks with divisible jobs

Ultimate performance limits to the aggregate processing speed of networks of processors that are processing a divisible job are described. These take the form of either closed-form expressions or numerical procedures to calculate the equivalent processing speed of an infinite number of processors. These processors are interconnected in either a linear daisy chain with load origination from the network interior or a tree topology. The tree topology is particularly general as a natural way to perform load distribution in a professor network topology with cycles (e.g., hypercube, toroidal network) is to use an embedded spanning tree. Such limits on performance are important as they provide an ideal baseline against which to compare the performance of finite configurations of processors.     

A resonant DC-DC transformer

The characteristics of a push-pull parallel resonant converter (PPRC) when operated as a DC-DC transformer were investigated theoretically and experimentally. In the DC-DC transformer region, the voltage transfer ratio of the PPRC was found to be practically constant and independent of the input voltage and load. In this mode, all the switching elements operate in the zero voltage switching (ZVS) condition. Another important feature of the proposed DC-DC transformer is the ability to drive it by an arbitrary switching frequency, provided that the latter is lower than the self-oscillating frequency. This permits the synchronization of the converter to a master clock. The analytical expressions for voltage and current stresses, as well as the other key parameters derived, are applied to develop design guidelines for the DC-DC transformer. The proposed topology was tested experimentally on a 100-W unit which was run in the 200-kHz frequency region     

Doppler tomography of O-type binaries: The physical properties of seven systems

We have analyzed UV photospheric lines of seven O-type binaries, by means of crosscorrelation and Doppler tomographic methods, with the goal of estimating the physical properties of the individual stars. These systems are HD 1337 (AO Cas), HD 47129 (Plaskett's star), HD 57060 (29 UW CMa), HD 37043 (Iota Ori), HD 215835 (DH Cep), HD 152218, and HD 152248. Mass ratios have been obtained primarily from a cross-correlation technique, but also by several other techniques. The tomographic techniques allow us to separate the spectra of the components. We then can estimate the individual spectral types and luminosity classes of the stars (and henceT _eff and logg, respectively), the luminosity ratio, and projected rotational velocities. We discuss the physical properties of these O-type binaries. These are some of the early results of a large scale project involving 36 O-type double-lined binary systems (from the catalog of Battenet al. 1989) which we will study using IUE and complementary ground-based data.     

Observing the Sun at 20–650 MHz at Thermopylae with Artemis

Fine structure of type IV radio solar bursts with a great variety and complexity often give much information in different ways and enable estimation of various coronal characteristics. In this work, we expose our new method for fine structure revealing and separation of two basic kinds of type IV fine structure, as fibers and pulsations. We also estimate frequency drift of fibers from dynamic spectra, clean from continuous background, with a prototype method using 2-D Fourier transform and we estimate periodicities of fibers as well as pulsations with continuous wavelet transform. Working with the last method we found periodicities close to 3 min umbral oscillations and 5 min global solar oscillations.     

Risk-based technology portfolio optimization for early space mission design

The successful design, development, and operation of space missions requires informed decisions to be made across a vast array of investment, scientific, technological, and operational issues. In the work reported in this paper, we address the problem of determining optimal technology investment portfolios that minimize mission risk and maximize the expected science return of the mission. We model several relationships that explicitly link investment in technologies to mission risk and expected science return. To represent and compute these causal and computational dependencies, we introduce a generalization of influence diagrams that we call inference nets. To illustrate the approach, we present results from its application to a technology portfolio investment trade study done for a specific scenario for the projected 2009 Mars MSL mission. This case study examines the impact of investments in precision landing and long-range roving technologies on the mission capability, and the associated risk, of visiting a set of preselected science sites. We show how an optimal investment strategy can be found that minimizes the mission risk given a fixed total technology investment budget, or alternatively how to determine the minimum budget required to achieve a given acceptable mission risk.     

Hard-constrained versus soft-constrained parameter estimation

The paper aims at contrasting two different ways of incorporating a priori information in parameter estimation, i.e., hard-constrained and soft-constrained estimation. Hard-constrained estimation can be interpreted, in the Bayesian framework, as maximum a posteriori probability (MAP) estimation with uniform prior distribution over the constraining set, and amounts to a constrained least-squares (LS) optimization. Novel analytical results on the statistics of the hard-constrained estimator are presented for a linear regression model subject to lower and upper bounds on a single parameter. This analysis allows to quantify the mean squared error (MSE) reduction implied by constraints and to see how this depends on the size of the constraining set compared with the confidence regions of the unconstrained estimator. Contrastingly, soft-constrained estimation can be regarded as MAP estimation with Gaussian prior distribution and amounts to a less computationally demanding unconstrained LS optimization with a cost suitably modified by the mean and covariance of the Gaussian distribution. Results on the design of the prior covariance of the soft-constrained estimator for optimal MSE performance are also given. Finally, a practical case-study concerning a line fitting estimation problem is presented in order to validate the theoretical results derived in the paper as well as to compare the performance of the hard-constrained and soft-constrained approaches under different settings     

Optimal Cooperative Sensing using a Team of UAVs

The authors investigates the joint optimal estimation of both the position and velocity of a ground moving target (GMT) using pulse Doppler radars on-board unmanned aerial vehicles (UAVs). The problem of cooperative estimation using a UAV team and the optimization of the team's configuration to achieve optimal GMT position and velocity estimates are addressed. Based on the Cramer-Rao bound, the minimum achievable error variance of the GMT position and velocity estimates is derived. The expression of the minimum achievable estimation error variance for unbiased estimation provided by the Cramer-Rao bound is minimized yielding the optimal configuration of the UAV team. Our solution is complete in that it addresses various GMT tracking scenarios and an arbitrary number of UAVs. Optimal sensor geometries for typical applications are illustrated