基于EM算法的最优斜高斯核设计

对于采用固定窗或核的时频分布，其适用的信号形式具有很大的局限性。基于信号的核能克服这样的缺点，斜高斯核是其中一类，为了得到基于不同信号的最优斜高斯核，本文提出采用ＥＭ算法根据实际信号的模糊函数估计斜高斯核的参数，以达到核的最优设计。     

一种新的多线性调频信号时频表示

本文提出了一种用于多线性调频信号时频表示的方法.首先由Radon-Ambiguity变换设计出线状核函数,以去除噪声和多线性调频信号之间的交叉项在模糊域中的影响,再通过二维傅立叶变换得到一种新的时频表示.仿真实验证明了此方法可以有效的去除噪声和多信号之间交叉项的影响,在低信噪比下也十分有效.     

一种新的抑制交叉项的时-频分布的分析

最近提出了一种新的时频分布，在保持高时频分辨率的同时可抑制交叉项，本文从信号模糊域滤波的角度分析了该分布中核函数的设计方法与思路，并针对多分量线性调频信号用该分布进行了计算机仿真。仿真结果证明了该分布在抑制交叉项并保持较高时频分辨率方面的有效性。     

用于多分量线性调频信号的自适应核分布分析

该文针对多分量线性调频信号,提出了一种新的自适应核时频分布-自适应高斯核分布,并给出了有效的核函数估计准则;以自适应高斯核分布为例,分析了采用自适应核时频分布对信号自身项及交叉项的影响,从而说明自适应核相对于固定核的优势所在;总结了基于模糊域自适应设计多分量线性调频信号核函数的一般方法。计算机仿真结果表明了自适应高斯核分布在抑制交叉项并保持较高时频分辨力方面的有效性。     

基于稀疏时频分布的跳频信号参数估计

基于常规时频分析方法的跳频信号参数估计中,采用核函数抑制时频分布交叉项会导致时频聚集性的下降,不利于信号参数提取。针对此问题,该文提出一种基于稀疏时频分布(STFD)的跳频信号处理方法。该方法首先根据Cohen类分布的原理和跳频信号模糊函数的特点,以模糊域矩形窗为核函数,构建了一种Cohen类的矩形核分布(RKD)。RKD可有效抑制交叉项,但其时频分辨率较低。为提高RKD的时频性能,在压缩感知框架下,利用跳频信号时频分布的稀疏特性,对RKD附加稀疏性约束,建立稀疏时频分布(STFD)的优化求解模型。STFD不仅能有效抑制交叉项,而且具有良好的时频聚集性。仿真分析表明,与传统时频分析方法相比,该文提出的基于STFD的跳频信号参数估计方法性能更优。     

基于乘积性模糊函数的多线性调频信号时频分布

本文提出一种新的多分量线性调频信号的双线性时频分布.在核函数设计中,提出了两种基于乘积性模糊函数的方法.两种方法在信号的模糊域能够有效地滤出噪声和交叉项,并保留绝大部分的自项能量,得到准确的核函数.仿真结果证实了该方法能够有效地抑制噪声和交叉项,提高时频分辨率,同时可以适应低信噪比环境.     

STFT在跳频信号分析中的应用

跳频信号分析一直是通信领域研究的热点，用时频分布来分析跳频信号是一种很有效的方法。时频分析有多种方法，其中小波变换时频分布对信号中夹杂的噪声非常敏感，维格纳威利分布虽然具有很好的时频聚集性，但分析多分量信号时存在严重的交叉干扰项。经典的STFT(Short Time Fourier Trans form)是一种很好的时频工具，本文对多种窗函数以及同一窗函数不同参数的STFT进行了Matlab仿真，仿真结果表明，选择合适的窗函数及其相关参数，会使STFT在跳频信号分析中取得令人满意的效果。     

基于乘积性模糊函数的核函数设计方法

利用乘积性模糊函数,提出一种双线性时频分布的核函数设计新方法,能够在模糊域有效地抑制噪声和多分量间的交叉项.在多分量强弱信号环境中,提出基于逐次减小误差的递归算法重构强信号,减小传统分析方法的残余信号影响和参数误差传播效应.利用分段近似的原则,可将非线性调频信号分解为多分量线性调频信号,从而将核函数设计方法推广到非线性调频信号环境.最后,仿真结果证实了该方法对多分量调频信号的有效性.     

典型非线性固定核函数时频分布的性能分析

传统的傅立叶变换无法满足对非线性、非平稳信号的分析,时频分析旨在构造一种时间和频率的密度函数,能够反映非平稳信号的时变特征,是非平稳信号分析的有力工具。文章讨论了魏格纳-威尔分布、伪平滑魏格纳-威尔分布及乔伊-威廉斯分布三种固定核函数时频分析方法的性能特征,进行计算机仿真,最后总结这三种分布在时频分辨率、抗干扰能力和信号适用性方面的优缺点。     

基于改进Choi-Williams分布的多分量信号频率识别方法

通过相关域内对短时信号模糊函数的分析,针对短时信号对Choi-Williams分布(CWD)的核函数进行了改进,提出对加窗信号运用改进后的CWD提取信号时频信息的方法。仿真实验表明陔方法对多分量信号具有较强的交叉项抑制能力,在提高了信噪比特性的同时,也减少了计算量。     

Rapid gridding reconstruction with a minimal oversampling ratio

Reconstruction of magnetic resonance images from data not falling on a Cartesian grid is a Fourier inversion problem typically solved using convolution interpolation, also known as gridding. Gridding is simple and robust and has parameters, the grid oversampling ratio and the kernel width, that can be used to trade accuracy for computational memory and time reductions. We have found that significant reductions in computation memory and time can be obtained while maintaining high accuracy by using a minimal oversampling ratio, from 1.125 to 1.375, instead of the typically employed grid oversampling ratio of two. When using a minimal oversampling ratio, appropriate design of the convolution kernel is important for maintaining high accuracy. We derive a simple equation for choosing the optimal Kaiser-Bessel convolution kernel for a given oversampling ratio and kernel width. As well, we evaluate the effect of presampling the kernel, a common technique used to reduce the computation time, and find that using linear interpolation between samples adds negligible error with far less samples than is necessary with nearest-neighbor interpolation. We also develop a new method for choosing the optimal presampled kernel. Using a minimal oversampling ratio and presampled kernel, we are able to perform a three-dimensional (3-D) reconstruction in one-eighth the time and requiring one-third the computer memory versus using an oversampling ratio of two and a Kaiser-Bessel convolution kernel, while maintaining the same level of accuracy.     

Optimal data-based kernel estimation of evolutionary spectra

Complex demodulation of evolutionary spectra is formulated as a two-dimensional kernel smoother in the time-frequency domain. First, a tapered Fourier transform, y_v(f, t), is calculated. Then the log-spectral estimate, is smoothed. As the characteristic widths of the kernel smoother increase, the bias from the temporal and frequency averaging increases while the variance decreases. The demodulation parameters, such as the order, length, and bandwidth of spectral taper and the kernel smoother, are determined by minimizing the expected error. For well-resolved evolutionary, spectra, the optimal taper length is a small fraction of the optimal kernel halfwidth. The optimal frequency bandwidth, w, for the spectral window scales as w²~λ/τ, where τ is the characteristic time and λ_F is the characteristic frequency scalelength. In contrast, the optimal halfwidths for the second stage kernel smoother scales as h~1/(τλ_F )¹(p+2)/ where p is the order of the kernel smoother. The ratio of the optimal-frequency halfwidth to the optimal-time halfwidth is determined     

二元裂解算子交替方向乘子法的核极限学习机

凸优化形式的核极限学习机(KELM)具有较高的分类准确率,但用迭代法训练凸优化核极限学习机要较传统核极限学习机的解线性方程法花费更长时间。针对此问题,该文提出一种2元裂解算子交替方向乘子法(BSADMM-KELM)来提高凸优化核极限学习机的训练速度。首先引入2元裂解算子,将求核极限学习机最优解的过程分裂为两个中间算子的优化过程,再通过中间算子的迭代计算而得到原问题的最优解。在22个UCI数据集上所提算法的训练时间较有效集法平均快29倍,较内点法平均快4倍,分类精度亦优于传统的核极限学习机;在大规模数据集上该文算法的训练时间优于传统核极限学习机。     

Optimal kernels for nonstationary spectral estimation

Current theories of a time-varying spectrum of a nonstationary process all involve, either by definition or by difficulties in estimation, an assumption that the signal statistics vary slowly over time. This restrictive quasistationarity assumption limits the use of existing estimation techniques to a small class of nonstationary processes. We overcome this limitation by deriving a statistically optimal kernel, within Cohen's (1989) class of time-frequency representations (TFR's), for estimating the Wigner-Ville spectrum of a nonstationary process. We also solve the related problem of minimum mean-squared error estimation of an arbitrary bilinear TFR of a realization of a process from a correlated observation. Both optimal time-frequency invariant and time-frequency varying kernels are derived. It is shown that in the presence of any additive independent noise, optimal performance requires a nontrivial kernel and that optimal estimation may require smoothing filters that are very different from those based on a quasistationarity assumption. Examples confirm that the optimal estimators often yield tremendous improvements in performance over existing methods. In particular, the ability of the optimal kernel to suppress interference is quite remarkable, thus making the proposed framework potentially useful for interference suppression via time-frequency filtering     

A signal-dependent time-frequency representation: fast algorithmfor optimal kernel design

A time-frequency representation based on an optimal, signal-dependent kernel has been previously been proposed in an attempt to overcome one of the primary limitations of bilinear time-frequency distributions: that the best kernel and distribution depend on the signal to be analyzed. The optimization formulation for the signal-dependent kernel results in a linear program with a unique feature: a tree structure that summarizes a set of constraints on the kernel. The authors present a fast algorithm based on sorting to solve a special class of linear programs that includes the problem of interest. For a kernel with Q variables, the running time of the algorithm is O(Q log Q), which is several orders of magnitude less than any other known method for solving this class of linear program. This efficiency enables the computation of the signal-dependent, optimal-kernel time-frequency representation at a cost that is on the same order as a fixed-kernel distribution. An important property of the optimal kernel is that it takes on essentially only the values of 1 and 0     

Removing interference components in time–frequency representations using morphological operators

Time–frequency representations have been of great interest in the analysis and classification of non-stationary signals. The use of highly selective transformation techniques is a valuable tool for obtaining accurate information for studies of this type. The Wigner-Ville distribution has high time and frequency selectivity in addition to meeting some interesting mathematical properties. However, due to the bi-linearity of the transform, interference terms emerge when the transform is applied over multi-component signals. In this paper, we propose a technique to remove cross-components from the Wigner-Ville transform using image processing algorithms. The proposed method exploits the advantages of non-linear morphological filters, using a spectrogram to obtain an adequate marker for the morphological processing of the Wigner-Ville transform. Unlike traditional smoothing techniques, this algorithm provides cross-term attenuations while preserving time–frequency resolutions. Moreover, it could also be applied to distributions with different interference geometries. The method has been applied to a set of different time–frequency transforms, with promising results.     

K插值单纯形法核极限学习机的研究

针对核极限学习机高斯核函数参数选优难,影响学习机训练收敛速度和分类精度的问题,该文提出一种K插值单纯形法的核极限学习机算法。把核极限学习机的训练看作一个无约束优化问题,在训练迭代过程中,用Nelder-Mead单纯形法搜索高斯核函数的最优核参数,提高所提算法的分类精度。引入K插值为Nelder-Mead单纯形法提供合适的初值,减少单纯形法的迭代次数,提高了新算法的训练收敛效率。通过在UCI数据集上的仿真实验并与其它算法比较,新算法具有更快的收敛速度和更高的分类精度。     

利用改进的半正定规划的支持向量机识别车牌

针对现有车牌识别系统效率低的问题,提出了一种改进的支持向量机算法。首先对车牌进行预处理和定位,将每个特征区域构建一个多核心组合。以半定规划求解最佳的权系数。使用改进的半定规划来解决多核学习算法,降低搜索空间。最后构建车牌识别模型。仿真实验表明,该算法效率高,稳定性好。     

基于类间距判据的高斯过程分类模型核参数选择方法研究

文中提出了一种基于类间距判据的高斯过程分类(GPC)模型核参数选择方法.将核参数作为自变量,类间距作为因变量,获得类间距随核参数变化的目标函数,然后采用共轭梯度法求取目标函数极值,最终获得核参数的最优值.实验表明,用DBTC作为判据进行核参数选择,分类正确率与原有参数选择方法基本相当,但GPC模型在进行参数选择时的耗时大幅减少,因而模型训练速度得到大幅提升.     

参数自适应决策的多分辨率核支持向量分类器

为克服经典支持向量分类器(SVC)训练算法中参数的选择需要多次人工调整的缺陷,本文提出了基于多分辨率核的支持向量机参数自适应调节策略。首先通过分析非线性核映射的特征空间超平面的最小VC维数,提出了多分辨率核函数参数的自适应优化准则。然后通过迭代求解获得最优泛化能力的多分辨率核参数数值。多分辨率核函数方法保持了经典SVC训练算法结构风险最小化的原则,克服了经典SVC选择单一参数的缺陷。仿真实验结果表明本文提出的算法能够自适应的选择合适的核参数达到最优泛化能力。