Dignet: an unsupervised-learning clustering algorithm forclustering and data fusion

Dignet is a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior-to-noise interference, when the noise does not exceed a prespecified level of tolerance. The complexity of the proposed ANN, in terms of neuron requirements versus stored patterns, increases linearly with the number of stored patterns and their dimensionality. The self-organization of Dignet is based on the idea of competitive generation and elimination of attraction well in the pattern space. Dignet is used for detection and distributed decision fusion. Analytical and numerical results are included     

Multimodule parallel series-loaded resonant converters

The design and implementation of a multimodule parallel series-loaded resonant (SLR) converter system is presented. The SLR converter to be paralleled is operated in the n=2 discontinuous mode (DCM). Its dc analysis and dynamic modeling are made. In parallel operation, an average control technique is proposed to compensate the mismatch in current control characteristics of each parallel converter. Good dynamic and static current sharing characteristics are obtained. In addition, to obtain good output voltage regulating control performance, a design procedure is presented to find the parameters of feedback voltage controller according to the prescribed specifications     

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.     

Simulation considers the human factors

Human factors aspects of flight simulation design at McDonnell Douglas are considered. Modeling approaches are examined and testing with pilots and cockpits is described.     

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.     

Performance comparison of PRF schedules for medium PRF radar

Previous work has shown how evolutionary algorithms (EAs) are an effective tool in optimising the selection of pulse repetition frequency (PRF) values of medium PRF schedules in an airborne fire control radar (FCR) application requiring target data in three PRFs. The optimisation is driven by the requirement to minimise range/Doppler blindness whilst maintaining full decodability. In this paper we detail work in which the optimisation process is applied to design novel short medium PRF schedules requiring target data in just two PRFs. The paper reports on the testing of a variety of near-optimum schedules to compare their blindness, decoding, and ghosting performances. The results show that in many situations, the 2 of N schedules are a practical alternative to conventional 3 of N processing.     

Performance of an adaptive feature-based processor for a wideband ground penetrating radar system

A two-stage algorithm for landmine detection with a ground penetrating radar (GPR) system is described. First, 3-D data sets are processed using a computationally inexpensive pre-screening algorithm which flags potential locations of interest. These flagged locations are then passed to a feature-based processor which further discriminates target-like anomalies from naturally occurring clutter. Current field trial (over 6500 square meters) and blind test results (over 39000 square meters) are presented and these show at least an order of magnitude improvement over other radar system-based detection algorithms on the same test lanes.     

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