基于分数低阶统计量的双谱及其估计方法

稳定分布可更好地描述实际中所遇到的具有显著脉冲特性的随机噪声.为了更好地抑制信号背景中的非高斯噪声,本文提出了基于分数低阶的双谱定义,并给出在分数低阶有色噪声背景下双谱非参数和参数模型的估计方法.仿真结果表明,同传统的双谱估计相比较,非参数法分数低阶双谱估计能有效的识别信号,保留了信号的幅度和相位信息,但存在较大的估计方差.基于AR模型的分数低阶双谱估计具有最大的谱平坦度,能够有效地抑制噪声,具有良好的韧性.     

基于分数低阶统计模型的自适应滤波器组谱估计方法

 本文研究了一些自适应滤波器组谱估计方法(AFSE)在对称alpha-stable噪声(SαS)环境下的性能.提出了三种新的基于分数低阶统计模型(FLOS)的自适应滤波器组谱估计方法(AFSE_-FLOS).这些新方法是基于分数低阶协方差(FLOC),因此在对称alpha-stable噪声环境下比基于二阶和一阶统计模型的方法估计性能好.计算机仿真结果表明这些基于分数低阶统计模型的新方法在对称alpha-stable噪声环境下的性能比对应的基于二阶和一阶统计模型的方法性能好,在高斯噪声环境下,与其对应的方法性能相似.     

基于DeepFM的校准游戏推荐系统研究

针对传统推荐模型只能提取低阶特征,不能挖掘高阶组合特征,且推荐结果往往没有考虑到Bias的问题,本文提出一种适用于游戏推荐场景的基于DeepFM的校准游戏推荐方法.此方法充分发挥了DeepFM能够挖掘低阶和高阶特征的能力,利用Steam平台的数据集训练优化模型,并进行了测试验证,同时还运用校准推荐对结果进行去偏得到最后...     

高阶微扰法的有效性研究

通过二维地、海面发射率的研究,研究了高、低阶微扰法的有效性,分析了不同极化方式、表面模型和介电常数的影响,得到了均方根高度的有效范围,给出了高斯模型的相关长度的匹配范围.结果表明,模型差异不影响低阶微扰法求解时均方根高度的有效值,但对高阶微扰法的结果影响很大;高斯模型下,高阶微扰法求解时相关长度的有效值远小于低阶时的结果.理论与矩量法计算及测量值的比较,证明了高阶微扰法的有效性.     

脉冲噪声环境下基于宽带模糊函数的双基地MIMO雷达目标参数估计新方法

以Alpha稳定分布作为噪声模型,研究了脉冲噪声环境下宽带双基地MIMO雷达系统中参数估计问题.针对在脉冲噪声环境中,基于传统的信号模型和算法效果显著退化的问题,本文提出了基于分数低阶统计量的宽带模糊函数算法.首先根据分数低阶宽带模糊函数的峰值点实现对多普勒频率尺度因子和时延的联合估计.接下来基于分数低阶宽带模糊函数构造两个子阵.通过采用改进的MUSIC算法和ESPRIT算法实现了收发角的联合估计.仿真实验表明本文算法具有很好的性能.     

稳定分布的水声信号处理新方法

本文依据分数低阶统计信号处理理论和舰船辐射噪声信号特性,提出了水声信号新的噪声模型,分析了常规LMS滤波方法在非高斯稳定分布噪声下性能退化的原因,提出了基于分数低阶p-范数的LMP,NLMP水声信号自适应降噪滤波方法,并分析了参数p值的选取对LMP滤波效果的影响.理论分析和计算机仿真结果表明这种方法在高斯和非高斯稳定分布条件下都具有良好的鲁棒性.     

基于分数低阶协方差的AR SαS模型α谱估计

根据自回归(AR) SαS模型的α谱,分析了基于分数低阶矩(FLOM)法估计AR SαS模型参数的不足.提出了一种基于分数低阶协方差(FLOC)的AR SαS模型参数估计方法,并给出了基于FLOC的AR SαS模型α谱方法.分别对AR SαS模型参数的估计、α稳定分布噪声中单一正弦信号的估计和两个正弦信号的分辨进行了仿真.仿真结果表明,基于FLOC的AR SαS模型α谱估计方法对于不同的α值均具有较好的韧性.特别是在α值较小,即α稳定分布噪声概率密度函数(PDF)拖尾比较严重时,本文所提出的基于FLOC的AR SαS模型α谱估计方法,其性能明显优于基于FLOM的AR SαS模型α谱估计方法.     

一种稳定的RLC互连π模型构建及其应用

基于RLC互连树节点导纳的低阶矩构建了一种稳定的互连π模型,并讨论了它在互连树延时和逻辑门延时估计中的应用.结果表明,该模型与已有方法相比精度有一定程度的提高.     

分数低阶噪声的频域处理及在通信中的应用

针对传统的信号处理中采用基于高斯分布与二阶统计量的方法会受到对信号噪声模型假设的限制,提出了非高斯分数低阶噪声理论与通信信号处理等领域的实际问题密切结合,改造现有的信号处理中的频域相关处理技术.本文介绍了α稳定分布模型与分数低阶矩理论,分析和总结了传统的信号处理方法在通信中的应用以及其极限性,对下一步的研究进行了总结与展望,分析了基于频域的分数阶噪声在通信中的应用前景.     

基于低阶近似分布源的目标方位估计

广义阵列流型描述的分布源模型,利用了一阶Taylor级数展开,使得模型与分布源空间能量分布形式无关,能够更加广泛地在实际中得到应用,称之为一阶近似分布源模型。研究表明,此种模型忽略高次Taylor级数项后带来的模型误差以及后续方位估计算法的性能损失,因此提出了二阶近似分布源模型,即利用二阶Taylor级数展开。新模型能够进一步减小模型误差,并且对相应的方位估计算法带来较大的性能提升。将分布源的一阶近似模型和二阶近似模型统称为低阶近似分布源模型。随后分析了低阶近似分布源模型在方位估计中的应用,提出了广义SMVDR算法。通过计算机仿真,验证了低阶近似分布源模型方位估计算法的性能,并且研究了分布源的低阶近似模型、空间频率模型和低阶Jacobi-Anger(JA)级数展开模型的模型误差。     

An order downdating algorithm for tracking system order andparameters in recursive least squares identification

A new time and order recursive method for on-line tracking of system order and parameters using recursive least squares (RLS) is presented. The method consists of two parts: a time updating portion that uses existing RLS inverse QR decomposition algorithms and a new computationally efficient “order downdating” portion that calculates the model parameters and residual error energies for an entire set of models with order varying from one to some prespecified maximum model order     

Model Order Reduction With Parametric Port Formulation

This paper presents a new method for obtaining the congruence transformation matrix for model order reduction of linear interconnect systems with large numbers of constrained ports. Model order reduction techniques have proven to be very effective for the simulation of large interconnect networks. However, using current model order reduction techniques, the size of the reduced system grows rapidly as the number of ports increases. This leads to a severe reduction in efficiency. In this paper, a new method is proposed, which is based on imposing practical restrictions on the loads that can be connected to the ports of the network. Specifically, constrained ports terminated by combined resistive and capacitive loads are considered. The reduction matrix is then computed while exploiting the information on the constrained ports and utilizing parametric model order reduction techniques, the resulting reduced model is significantly smaller than standard reduced models. Examples are presented that demonstrate the accuracy and efficiency of the new method.     

New autoregressive (AR) order selection criteria based on the prediction error estimation

The most important problem in data modeling using the AR model is the order selection. Some AR order selection criteria estimate the prediction error and choose the order that minimizes this estimated prediction error. All of these criteria use the same formula for estimating the prediction error from the residual variance for all AR models. However, experimental results show that the relationship between the prediction error and the residual variance depends on the AR model. In this paper, we introduce new formulas for estimating the prediction error using the residual variance. These formulas depend on the AR model, and are obtained through assuming a white Gaussian noise as the input noise to the AR model and assuming that the least-squares-forward (LSF) method is used for estimating the AR coefficients. The performance of the new order selection criteria introduced in this paper is compared with other AR order selection criteria using simulated data. Results show that the new criteria have good performance in estimating the prediction error and in selecting an appropriate order for the AR model.     

稳定分布下基于FAM的低阶循环谱算法研究

该文在α稳定分布下结合共变理论、循环平稳和分数低阶矩(FLOM)等理论和方法,提出基于FAM (FFT Accumulation Method)的低阶循环谱算法,对算法中存在的循环泄露现象及算法复杂度进行了分析,并以调制信号(AM,QPSK)做了实验仿真.结果表明在α稳定分布下循环平稳信号的低阶循环谱密度和在高斯模型下的二阶循环谱结构是一致的,但基于α稳定分布假定所设计的信号处理算法对信号噪声特性不确定情况具有较好的韧性和抗脉冲噪声性能.最后在循环谱域构造了调制信号的低阶循环特征参数,为复杂背景下的调制识别提供了新的途径.     

An efficient terminal and model order reduction algorithm

The paper proposes an efficient terminal and model order reduction method for compact modeling of interconnect circuits with many terminals. The new method is inspired by the recently proposed terminal reduction method, SVDMOR [P. Feldmann, F. Liu, Sparse and efficient reduced order modeling of linear subcircuits with large number of terminals, in: Proceedings of the International Conference on Computer Aided Design (ICCAD), 2004, pp. 88-92]. But different from SVDMOR, the new method considers higher order moment information for terminal responses during the terminal reduction and separately applies singular value decomposition (SVD) on both input and output terminals for low-rank approximations. This is in contrast to the SVDMOR method where input and output terminal responses are approximated by SVD at the same time, which can lead to large errors when the numbers of inputs and outputs are quite different. We analyze the passivity requirements for SVD-based terminal and model order reduction and show that the combined passive terminal and MOR using SVD method will not lead an effective terminal reduction in general. Our experimental results show that the proposed ESVDMOR method outperforms the SVDMOR method in terms of accuracy for the same reduced model sizes when the numbers of input and output terminals are quite different.     

Hierarchical Krylov subspace based reduction of large interconnects

In this paper, we propose a new model order reduction approach for large interconnect circuits using hierarchical decomposition and the Krylov subspace projection-based model order reduction methods. The new approach, called hiePrimor, first partitions a large interconnect circuit into a number of smaller subcircuits and then performs the projection-based model order reduction on each of subcircuits in isolation and on the top-level circuit thereafter. The new approach is very amenable for exploiting the multi-core based parallel computing platforms to significantly speed up the reduction process. Theoretically we show that hiePrimor can deliver the same accuracy as the flat reduction method given the same reduction order and it can also preserve the passivity of the reduced models as well. We also show that partitioning has large impacts on the performance of hierarchical reduction and the minimum-span objective should be required to attain the best performance for hierarchical reduction. The proposed method is suitable for reducing large global interconnects like coupled bus, transmission lines, large clock nets in the post-layout stage. Experimental results demonstrate that hiePrimor can be significantly faster and more scalable than the flat projection methods like PRIMA and be order of magnitude faster than PRIMA with parallel computing without loss of accuracy. Interconnect circuits with up to 4 million nodes can be analyzed in a few minutes even in Matlab by the new method.     

一种基于可重用模型组件的战场目标信源建模方法

战场情报仿真缺乏典型通用目标信源建模方法,难以为后续仿真流程中新概念、新算法的演示验证提供各种规范、完备的战场目标信源建模支撑.为了构建灵活通用、要素完备的典型战场目标信源,提出了基于对象的模版化、组件化的信源建模方法.从目标静态数据模型、目标动态行为模型两个方面进行数字化建模抽象,以模型驱动仿真数据生成,实现战场情报仿真环境下各种异构信源的体系化建模.试验证明该建模方法具有良好实用性,所建立的目标信源仿真模型可灵活组装、自由编配,为情报和作战领域的信源仿真提供了一种灵活、通用的目标建模方法.     

指数阻尼正弦模型阶选择算法

 本文针对指数阻尼正弦信号模型提出了一种新的基于子空间和结构总体最小范数的阶选择算法.该方法利用信号子空间移不变性以及阶与信号分量数匹配时总体误差最小原理建立模型阶估计准则.实验表明该方法获得了更高的阶估计精度.算法无需设置阈值和惩罚项,自动完成阶估计过程.它可以应用到高分辨率的谐波恢复算法中.     

A new perturbation analysis for signal enumeration in rotational invariance techniques

The ESPRIT algorithm is a subspace-based high-resolution method used in source localization and spectral analysis, which provides very accurate estimates of the signal parameters. However, the underlying theory assumes a known model order, which is usually not the case in many applications. In particular, it is well known that underevaluating the model order biases the estimation. In this paper, we analyze the perturbation induced by an erroneous model order, and we present an error bound for the estimated parameters. Based on this theoretical framework, we propose a new method for selecting an appropriate modeling order, which consists in minimizing the error bound. This approach is applied to both synthetic and musical signals, and its performance is compared to that of existing methods, such as the information theoretic criteria.     

A New FDTD Formulation for Wave Propagation in Biological Media With Cole–Cole Model

The finite-difference time-domain (FDTD) method has been widely used to simulate the electromagnetic wave propagation in biological tissues. The Cole-Cole model is a formulation which can describe many types of biological tissues accurately over a very wide frequency band. However, the implementation of the Cole-Cole model using the FDTD method is difficult because of the fractional order differentiators in the model. In this letter, a new FDTD formulation is presented for the modeling of electromagnetic wave propagation in dispersive biological tissues with the Cole-Cole model. The Z-transform is used to represent the frequency dependent dielectric properties. The fractional order differentiators in the Cole-Cole model is approximated by a polynomial. The coefficients of the polynomial are found using a least-squares fitting method