Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.     

Optimal motion planning for dual-spacecraft interferometry

We address the question of design and optimal control of a class of dual-spacecraft interferometric imaging formations. The first main contribution is that we combine two ideas introduced separately in the literature and propose a maneuver that offers improved imaging performance. We then formulate an optimal control problem to minimize fuel consumption and maximize image quality by minimizing the relative speed, which is proportional to the signal-to-noise ratio (SNR) of the reconstructed image. We show that the necessary conditions are also sufficient and that the resulting optimal control is unique. Finally, we apply a continuation method to solve for the unique optimal trajectory.     

The Pre-CME Sun

The coronal mass ejection (CME) phenomenon occurs in closed magnetic field regions on the Sun such as active regions, filament regions, transequatorial interconnection regions, and complexes involving a combination of these. This chapter describes the current knowledge on these closed field structures and how they lead to CMEs. After describing the specific magnetic structures observed in the CME source region, we compare the substructures of CMEs to what is observed before eruption. Evolution of the closed magnetic structures in response to various photospheric motions over different time scales (convection, differential rotation, meridional circulation) somehow leads to the eruption. We describe this pre-eruption evolution and attempt to link them to the observed features of CMEs. Small-scale energetic signatures in the form of electron acceleration (signified by nonthermal radio bursts at metric wavelengths) and plasma heating (observed as compact soft X-ray brightening) may be indicative of impending CMEs. We survey these pre-eruptive energy releases using observations taken before and during the eruption of several CMEs. Finally, we discuss how the observations can be converted into useful inputs to numerical models that can describe the CME initiation.     

Detection of small objects in clutter using a GA-RBF neural network

Detection of small objects in a radar or satellite image is an important problem with many applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to reconstruct the chaotic dynamic of sea clutter. The reconstructed sea clutter dynamical system which usually takes the form of a nonlinear predictor does not only provide a model of the sea scattering phenomenon, but it can also be used to detect the existence of small targets such as fishing boats and small fragments of icebergs by observing abrupt changes in the prediction error. We applied a genetic algorithm (GA) to obtain an optimal reconstruction of sea clutter dynamic based on a radial basis function (RBF) neural network. This GA-RBF uses a hybrid approach that employes a GA to search for the optimum values of the following RBF parameters: centers, variance, and number of hidden nodes, and uses the least square method to determine the weights. It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net (i.e., no approximation error), this GA-RBF approach can reconstruct the exact dynamic from its time series measurements. In addition to the improved accuracy in modeling sea clutter dynamic, the GA-RBF is also shown to enhance the detectability of small objects embedded in the sea. Using real-life radar data that are collected in the east coast of Canada by two different radar systems: a ground-based radar and a satellite equipped with synthetic aperture radar (SAR), we show that the GA-RBF network is a reliable detector for small surface targets in various sea conditions and is practical for real-life search and rescue, navigation, and surveillance applications     

混合移频静刚度法和直接扰动迭代法

本文对混合移频动柔度法的失代式进行等价变换，从而得到一个“变种”，该变种不包含混合移频系统的动柔度式F^△（λ*），这便消除了F^△（λ*）带来的近似性，另外，对直接扰动法也进行等价变换，引出可以省时地用于许多特征向量导数计算的直接扰动迭代法。最后，为了供读者参数，本文还导出了三种混合移频动柔度法的非迭代式，其中有两种恰好是另外两种动柔度法经混合移频后的非迭代式，这实际上已将另两种动柔度法也扩展到密集根情况了。     

密集根之特征向量导数的改进高精度动柔度法

作者曾为许多特征向量导数计算，提出过一种介于直接法（指直接求解线代方程组的，以Nelson为代表的一类方法）和模态法之间的高精度动柔度法。这种方法与作者建立的一般动柔度法一样，不能用于密集根之特征向量导数的计算。为使该方法扩展到密集根状态，本文将作者发展的混合移频技术应用于原高精度动柔度法，并重新推导了混合移频系统 的高精度动柔度式，从而形成能适用于许多密集根之特征向量导数计算的改进高精度动柔度法。     

The characteristics of sharp (small-scale) boundaries of solar wind plasma and magnetic field structures

We investigate properties of large (>20%) and sharp (<10 min) solar wind ion flux changes using INTERBALL-1 and WIND plasma and magnetic field measurements from 1996 to 1999. These ion flux changes are the boundaries of small-scale and middle-scale solar wind structures. We describe the behavior of the solar wind velocity, temperature and interplanetary magnetic field (IMF) during these sudden flux changes. Many of the largest ion flux changes occur during periods when the solar wind velocity is nearly constant, so these are mainly plasma density changes. The IMF magnitude and direction changes at these events can be either large or small. For about 55% of the ion flux changes, the sum of the thermal and magnetic pressure are in balance across the boundary. In many of the other cases, the thermal pressure change is significantly more than the magnetic pressure change. We also attempted to classify the types of discontinuities observed.