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.     

双时间步长加权ENO-强紧致高分辨率格式及在叶轮机械非定常流动中的应用

在非结构网格下,针对非定常三维N-S方程组发展了一种双时间步长高精度快速迭代格式。该格式在时间上具有二阶精度,在空间上将r=3的加权ENO格式与强紧致格式相结合去处理N-S方程中的对流项以及离散方程的右端项,并用四阶精度的紧致格式去计算N-S方程中的粘性项。典型的3个算例从不同侧面对格式进行了考核。计算表明:该算法具有高效率与高分辨率的特征,所得的计算结果与相关实验数据比较吻合,初步表明了该算法可以在非结构网格下模拟非定常流动的物理过程。      

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.     

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     

Satellite Observations of Natural and Anthropogenic Aerosol Effects on Clouds and Climate

Anthropogenic aerosols affect the climate system and the hydrological cycle. The net effect of aerosols is to cool the climate system, directly by reflecting sunlight to space, and indirectly by increasing the brightness and cover of clouds that in turn also reflect more sunlight to space. The uncertainty in the aerosol effect on climate is 5 times greater than that of the greenhouse gases. The reason for this is the short aerosol lifetime and chemical complexity, that makes it difficult to represent the global aerosol budget from surface or aircraft measurements. Satellites provide daily global information about the aerosol content, generating large statistics with excellent regional and global representation of the aerosol column concentration, and differentiating fine from coarse aerosol. Here we use observations performed with the MODIS instrument onboard the Terra and Aqua satellites to differentiate natural from anthropogenic aerosols, and to measure the aerosol effect on cloud properties and on the reflectivity of sunlight.     

Investigation of the Influence of Magnetic Anomalies on Ion Distributions at Mars

Using data from the Mars Express Ion Mass Analyzer (IMA) we investigate the distribution of ion beams of planetary origin and search for an influence from Mars crustal magnetic anomalies. We have concentrated on ion beams observed inside the induced magnetosphere boundary (magnetic pile-up boundary). Some north-south asymmetry is seen in the data, but no longitudinal structure resembling that of the crustal anomalies. Comparing the occurrence rate of ion beams with magnetic field strength at 400 km altitude below the spacecraft (using statistical Mars Global Surveyor results) shows a decrease of the occurrence rate for modest (< 40 nT) magnetic fields. Higher magnetic field regions (above 40 nT at 400 km) are sampled so seldom that the statistics are poor but the data is consistent with some ion outflow events being closely associated with the stronger anomalies. This ion flow does not significantly affect the overall distribution of ion beams around Mars.     

Obstacle detection by recognizing binary expansion patterns

A technique is described for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to “safe” or “dangerous” situations. We show that the essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns     

Terminal control monitor for ATC using knowledge-based system

A knowledge-based system for air traffic control (ATC) in terminal control regions is designed for improving service and operation. Domain knowledge is established following rules, regulations, and comments from controllers. An expert system is adopted for approach control and airport control in the terminal control region. The proposed system was implemented in PC-AT with Turbo Prolog. The air terminal control monitor was checked with regard to flight information input, real-time data refreshment, air traffic rescheduling, constraint violation double checking, emergency responses, and normal monitoring. Simulation results are given