Training astronauts using three-dimensional visualisations of the International Space Station

Recent advances in personal computer technology have led to the development of relatively low-cost software to generate high-resolution three-dimensional images. The capability both to rotate and zoom in on these images superposed on appropriate background images enables high-quality movies to be created. These developments have been used to produce realistic simulations of the International Space Station on CD-ROM. This product is described and its potentialities demonstrated. With successive launches, the ISS is gradually built up, and visualised over a rotating Earth against the star background. It is anticipated that this product's capability will be useful when training astronauts to carry out EVAs around the ISS. Simulations inside the ISS are also very realistic. These should prove invaluable when familiarising the ISS crew with their future workplace and home. Operating procedures can be taught and perfected. "What if" scenario models can be explored and this facility should be useful when training the crew to deal with emergency situations which might arise. This CD-ROM product will also be used to make the general public more aware of, and hence enthusiastic about, the International Space Station programme.     

Pursuer identification and time-to-go estimation using passive measurements from an evader

We present an algorithm for identifying the parameters of a proportional navigation guidance missile (pursuer) pursuing an airborne target (evader) using angle-only measurements from the latter. This is done for the purpose of classifying the missile so that appropriate counter-measures can be taken. Mathematical models are constructed for a pursuer with a changing velocity, i.e., a direction change and a speed change. Assuming the pursuer is launched from the ground with fixed thrust, its motion can be described by a four-dimensional parameter vector consisting of its proportional navigation constant and three parameters related to thrusting. Consequently, the problem can be solved as a parameter estimation problem, rather than state estimation and we provide an estimator based on maximum likelihood (ML) to solve it. The parameter estimates obtained can be mapped into the time-to-go until intercept estimation results are presented for different scenarios together with the Cramer-Rao lower bound (CRLB), which quantifies the best achievable estimation accuracy. The accuracy of the time-to-go estimate is also obtained. Simulation results demonstrate that the proposed estimator is efficient by meeting the CRLB.     

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.     

Dynamic Cramer-Rao bound for target tracking in clutter

Recently, there have been several new results for an old topic, the Cramer-Rao lower bound (CRLB). Specifically, it has been shown that for a wide class of parameter estimation problems (e.g. for objects with deterministic dynamics) the matrix CRLB, with both measurement origin uncertainty (i.e., in the presence of false alarms or random clutter) and measurement noise, is simply that without measurement origin uncertainty times a scalar information reduction factor (IRF). Conversely, there has arisen a neat expression for the CRLB for state estimation of a stochastic dynamic nonlinear system (i.e., objects with a stochastic motion); but this is only valid without measurement origin uncertainty. The present paper can be considered a marriage of the two topics: the clever Riccati-like form from the latter is preserved, but it includes the IRF from the former. The effects of plant and observation dynamics on the CRLB are explored. Further, the CRLB is compared via simulation to two common target tracking algorithms, the probabilistic data association filter (PDAF) and the multiframe (N-D) assignment algorithm.     

Temporal and Spatial Variation of the Ion Composition in the Ring Current

A global view of the ring current ions is presented using data acquired by the instrument MICS onboard the CRRES satellite during solar maximum. The variations of differential intensities, energy spectra, radial profile of the energetic particles and the origin of the magnetic local time (MLT) asymmetry of the ring current have been investigated in detail. O⁺ ions are an important contributor to the storm time ring current. Its abundance in terms of number density increases with increasing geomagnetic activity as well as its energy density. However, a saturation value for the energy density of O⁺ ions has been found. The low-energy H⁺ ions show a dramatic intensification and a rapid decay. However, its density ratio during the storm maximum is almost constant. On the other hand, high-energy H⁺ ions first exhibit a flux decrease followed by a delayed increase. Its density ratio shows an anti-correlation with the storm intensity. Both the positions of the maximum flux of O⁺ and He⁺ depend on storm activity: they move to lower altitudes in the early stage of a storm and move back to higher L-values during the recovery phase. Whereas the position of H⁺ and He⁺⁺ show almost no dependence on the Dst index. The energy density distributions in radial distance and magnetic local time show drastic differences for different ion species. It demonstrates that the ring current asymmetry mainly comes from oxygen and helium ions, but not from protons. The outward motion of O⁺ around local noon may have some implications for oxygen bursts in the magnetosheath during IMF Bz negative conditions as observed by GEOTAIL.     

Adaptive boosting for SAR automatic target recognition

The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature     

Efficient fault tolerant estimation using the IMM methodology

Space systems are characterized by a low-intensity process noise resulting from uncertain forces and moments. In many cases, their scalar measurement channels can be assumed to be independent, with one-dimensional internal dynamics. The nominal operation of these systems can be severely damaged by faults in the sensors. A natural method that can be used to yield fault tolerant estimates of such systems is the interacting multiple model (IMM) filtering algorithm, which is known to provide very accurate results. However, having been derived for a general class of systems with switching parameters, the IMM filter does not utilize the independence of the measurement errors in different channels, nor does it exploit the fact that the process noise is of low intensity. Thus, the implementation of the IMM in this case is computationally expensive. A new estimation technique is proposed herein, that explicitly utilizes the aforementioned properties. In the resulting estimation scheme separate measurement channels are handled separately, thus reducing the computational complexity. It is shown that, whereas the IMM complexity is exponential in the number of fault-prone measurements, the complexity of the proposed technique is polynomial. A simulation study involving spacecraft attitude estimation is carried out. This study shows that the proposed technique closely approximates the full-blown IMM algorithm, while requiring only a modest fraction of the computational cost.     

Application of the Kalman-Levy Filter for Tracking Maneuvering Targets

Among target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an interacting multiple model (IMM) estimator). The oversimplification resulting from the above assumptions can cause degradation in tracking performance. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. This filter assumes a heavy-tailed noise distribution known as the Levy distribution. Due to the heavy-tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of the Kalman-Levy filter in the nonmaneuvering portion of track is worse than that of a Kalman filter. For this reason, an IMM with one Kalman and one Kalman-Levy module is developed here. Also, the superiority of the IMM with Kalman-Levy module over only Kalman-filter-based IMM for realistic maneuvers is shown by simulation results.     

Space Interferometry Mission spacecraft pointing error budgets

Preliminary error budgets for the pointing knowledge, control, and stability of the Space Interferometry Mission (SIM) spacecraft are constructed using the specifications of commercial off-the-shelf attitude determination sensors, attitude control actuators, and other spacecraft capabilities that have been demonstrated in past missions. Results obtained indicate that we can meet all the presently known spacecraft pointing requirements. A large number of derived requirements are generated from this study. Examples are specifications on attitude determination sensors, attitude control actuators, minimum settling time after a rest-to-rest spacecraft slew. Preliminary error budgets constructed in this study must be updated to reflect the changing spacecraft design and requirements     

Time variations of cosmic-ray helium isotopes with BESS-Polar I

The Balloon-borne Experiment with a Superconducting Spectrometer (BESS) is configured with a solenoidal superconducting magnet and a suite of precision particle detectors, including time-of-flight hodoscopes based on plastic scintillators, a silica-aerogel Cherenkov detector, and a high resolution tracking system with a central jet-type drift chamber. The charges of incident particles are determined from energy losses in the scintillators. Their magnetic rigidities (momentum/charge) are measured by reconstructing each particle trajectory in the magnetic field, and their velocities are obtained by using the time-of-flight system. Together, these measurements can accurately identify helium isotopes among the incoming cosmic-ray helium nuclei up to energies in the GeV per nucleon region. The BESS-Polar I instrument flew for 8.5 days over Antarctica from December 13th to December 21st, 2004. Its long-duration flight and large geometric acceptance allow the time variations of isotopic fluxes to be studied for the first time. The time variations of helium isotope fluxes are presented here for rigidities from 1.2 to 2.5 GV and results are compared to previously reported proton data and neutron monitor data.