Adaptive GLR-, Rao- and Wald-based CFAR detectors for a subspace signal embedded in structured Gaussian interference

The problem of detecting a subspace signal embedded in subspace Gaussian interference and thermal noise is studied in this paper. In this problem, both the signal-independent and signal-dependent interferences are assumed to be present, therefore the overall interference subspace covers the signal subspace. The approach of this paper extends previous works involving either of those two kinds of interferences. A set of secondary data containing only interference plus noise is employed to estimate the interference covariance matrix and the noise power. Three new detectors are designed via the generalized likelihood ratio (GLR), Rao and Wald tests, respectively. Their probabilities of false alarms (PFAs) and detections are analytically derived. The PFAs show that the new detectors have the constant false alarm rate (CFAR) property against the interference and noise. Numerical results show that the new detectors outperform their counterparts for the studied problem. Furthermore, the new detectors are less sensitive to the secondary data size and to the mismatched subspace signal than some other detectors, such as the GLR detector (GLRD), the adaptive matched filter (AMF), the adaptive subspace detector (ASD), etc.     

On global Hessenberg based methods for solving Sylvester matrix equations

In the first part of this paper, we investigate the use of Hessenberg-based methods for solving the Sylvester matrix equation $AX+XB=C$. To achieve this goal, the Sylvester form of the global generalized Hessenberg process is presented. Using this process, different methods based on a Petrov–Galerkin or on a minimal norm condition are derived. In the second part, we focus on the SGl-CMRH method which is based on the Sylvester form of the Hessenberg process with pivoting strategy combined with a minimal norm condition. In order to accelerate the SGl-CMRH method, a preconditioned framework of this method is also considered. It includes both fixed and flexible variants of the SGl-CMRH method. Moreover, the connection between the flexible preconditioned SGl-CMRH method and the fixed one is studied and some upper bounds for the residual norm are obtained. In particular, application of the obtained theoretical results is investigated for the special case of solving linear systems of equations with several right-hand sides. Finally, some numerical experiments are given in order to evaluate the effectiveness of the proposed methods.     

Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real-time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi-analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.     

Research on the identification performance of UHF RFID system considering the correlation of cascaded channel

The identification rate of UHF RFID system was restricted by multipath propagation effects.The system identification performance was studied considering the correlation coefficient between forward and reverse channels.Based on the generalized Rician fading channel model,the analytical expression of identification rate was derived under independent,full correlation and correlation cases.Compared with the existing analysis,the proposed uniform calculation formula of identification rate was for any correlation coefficient and kinds of channel conditions.The numerical computation and Monte-carlo simulations show that the influences of different correlation coefficients,channel conditions,sensitivity and distance on the identification rate.     

Waves in generalized thermo-viscoelastic infinite continuum with cylindrical cavity due to three-phase-lag time-nonlocal heat transfer

Abstract

Generalized thermoelastic interactions due to three-phase-lag time-nonlocal heat transfer in a Kelvin-Voigt type infinitely extended visco-thermoelastic continuum with cylindrical cavity has been investigated. The two-temperature generalized thermoelasticity theory has also been taken into account. The problem has been solved in the domain of Laplace on the assumption that the surface of the cavity is free from traction and is subjected to a smooth and time-dependent-heating effect. Laplace inversion of the transformed solutions has been carried out numerically. The obtained numerical data for different considerations are plotted in graphs to study the effects of time-nonlocal parameter, two-temperature parameter and visco-thermoelastic relaxation parameter on different thermoelastic quantities of physical interest.     

基于交会图和多元统计法的神经网络技术在火山岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用BP神经网络技术对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是样本集和初始权重的建立,以及模型的优选。本文提出了一种基于交会图和多元统计法的学习样本生成方法,即根据取心岩样的地球化学和岩石学研究,用交会图技术建立样本集,采用聚类分析和距离判别法确定初始权重。将研究方法应用在松辽盆地杏山地区火山岩岩性识别问题中,取得了很好的效果,岩性解释符合率高于90%。文中通过四种岩性识别处理模式的对比研究,表明赋权重处理模式为最优处理模型。在神经网络模型预测过程中,需充分利用已有的地质经验和测井曲线信息建立典型可靠的样本文件,同时考虑神经网络方法中各种因素的影响,优选模型和计算参数才能使预测结果符合实际情况。     

地统计克立格插值法在工程土方计算中的应用

土方计算是工程中经常碰到的问题。尝试利用地统计学的克立格插值法来计算土方，将其应用于余姚市泗门镇土地整理工程中进行实测试验。介绍了其基本的原理和公式，并与利用数字高程模型计算得到的土方工程量进行了比较，发现地统计插值法可较精确地确定设计标高和土方量，证明该方法的可行性和优越性。     

面积坐标法构造含转角自由度的四结点膜元

以四边形面积坐标作为工具,构造了两个含转角自由度的广义协调四边形单元AQ4和lAQ4。它们通过强式分片检验,与同类单元相比,具有很高的计算精度,能消除梯形闭锁现象,有很强的抗网格畸变的能力。     

MIMO系统的约束广义预测控制

讨论了输出终端带不等式约束情形的多输入多输出系统，说明了这些约束可以通过修改输入柔化系数而使其得以满足，在柔化系数的选择范围确定过程中，为了避免矩阵求逆，采用利用已知数据辨识参数的方法来确定柔化系数，从而使得问题简化。降低了计算量且加快了收敛速度。