基于小波特征的星系光谱分类

提出了一种新的星系光谱分类方法.首先,对原始光谱进行四级小波分解,选择主要包含谱线信息的第四级小波系数作为光谱的小波特征;然后,利用主分量分析对光谱的小波特征进行特征压缩,得到光谱的识别特征;最后,利用Fisher线性判别分析实现分类.该方法能够在红移值未知的情况下,对流量未定标的星系光谱进行识别.通过实验与其他几种分类方法进行了比较.实验结果表明,本文方法具有较强的鲁棒性,在流量未定标情况下的识别效果优于其他几种分类方法.     

飞秒激光诱导Zn等离子体发射光谱时间演化特性

利用增强电荷耦合器(ICCD)光谱探测系统对飞秒激光诱导的Zn等离子体发射光谱进行时间分辨的采集和分析,研究飞秒激光等离子体光谱及其参量的时间演化特性。分析Zn等离子体的连续谱和特征谱的谱线强度随时间的演化,发现连续谱先出现且寿命只有100 ns,随后出现特征谱,对应于不同跃迁的谱强度不同。同时由谱线的展宽和强度及其跃迁能级的相关参数等得到电子密度和温度随时间的演化规律。对谱线频移进行了分析,研究发现在等离子体膨胀初期Zn原子特征谱线(Zn I)481.0 nm的特征谱线存在较大红移,可达到0.23 nm,延时300 ns后,红移变得很小。频移随电子密度的变化近似呈线性关系。     

一种改进的雷达辐射源识别在线学习方法

针对支持向量机分类器学习新增样本知识实时性差的问题,本文研究了一种基于壳向量和Parzen窗密度估计的雷达辐射源识别在线学习方法.通过Parzen窗密度估计剔除样本孤立野点,通过求取样本集的壳向量缩短训练时间,利用训练好的分类器完成雷达信号样本识别.仿真实验表明,提出的基于壳向量和Parzen窗密度估计的雷达辐射源识别...     

离散频谱梯形窗幅度比值校正法的频率估计

用FFT对正弦信号进行频率估计时,经常会遇到栅栏效应和频谱泄露的问题,为了提高正弦信号的频率估计精度,提出了一种离散频谱梯形窗幅度比值校正法。通过建立梯形窗的信号估计模型,并结合主瓣内最高谱线和次高谱线两条谱线的幅度比值关系,得到了加梯形窗的离散频谱校正公式。利用该算法对单频正弦信号的频谱校正误差进行了仿真,并且与加矩形窗时的频谱校正误差进行了比较。仿真结果表明,在信噪比较低时,该方法的频谱校正误差小,其性能明显优于加矩形窗的频谱校正精度。     

基于Renyi熵的非线性系统中传感器管理算法

针对高斯非线性系统中的多目标跟踪问题,提出了一种基于Renyi熵的传感器管理算法。该算法首先根据无迹卡尔曼滤波计算预测误差与滤波误差,度量目标跟踪精度要求;利用Renyi熵结合Parzen窗函数对概率密度函数进行近似估计,得到目标的信息增量,以此作为代价函数。同时,引入目标优先级(即威胁度),得到效能函数,形成传感器管理模型;最后利用该模型实现了传感器资源的分配。仿真结果表明,该算法利用Renyi熵可以表达非线性系统高阶特性的特点,结合Parzen窗函数,保持精度的同时减少运算量,较好地度量了跟踪过程中信息的不确定性,降低了跟踪误差,优化了系统的跟踪性能。     

基于RANSAC和三维谱峰分析的全姿态散射中心建模

全姿态散射中心模型是一种性能优良的光学区复杂目标电磁散射参数化模型。针对传统的基于候选点筛选和聚类的全姿态散射中心建模方法易出现虚假散射中心和遗漏真实散射中心的问题,该文提出了一种基于目标三维空间电磁散射强度场谱峰分析的建模方法。首先,基于目标多视一维散射中心参数,利用随机采样一致性(RANSAC)方法和Parzen窗函数方法估计目标在三维空间中的电磁散射强度场。然后,通过谱峰分析、散射中心关联和多视量测融合,得到全姿态三维散射中心的位置。最后,利用二值形态学处理修正全姿态散射中心的角度可见性,估计全姿态散射中心的散射系数和类型参数。仿真结果表明,该文方法所提取的全姿态散射中心与目标几何结构具有极强的关联性,相较传统方法,在缩减三维散射中心数量的同时提升了模型的表示精度。     

基于 Renyi熵的非线性系统中传感器管理算法

针对高斯非线性系统中的多目标跟踪问题,提出了一种基于Renyi熵的传感器管理算法。该算法首先根据无迹卡尔曼滤波计算预测误差与滤波误差,度量目标跟踪精度要求;利用Renyi熵结合Parzen窗函数对概率密度函数进行近似估计,得到目标的信息增量,以此作为代价函数。同时,引入目标优先级（即威胁度）,得到效能函数,形成传感器管理模型;最后利用该模型实现了传感器资源的分配。仿真结果表明,该算法利用Renyi熵可以表达非线性系统高阶特性的特点,结合Parzen窗函数,保持精度的同时减少运算量,较好地度量了跟踪过程中信息的不确定性,降低了跟踪误差,优化了系统的跟踪性能。     

大Doppler频移条件下基于导频信号的扩频码捕获

分析了Doppler频移对传统扩频码捕获算法的影响,提出了在发送导频信号期间,利用自适应谱线增强器(ALE)对部分匹配滤波输出进行处理以估计Doppler频移值,经过频偏校正后,采用传统全匹配捕获算法进行扩频码捕获判决的新方法。仿真结果表明,这种方法可以有效地消除大Doppler频移的影响,迅速完成扩频码的捕获。     

基于局部相对形状上下文与Q-谱的点模式匹配算法

本文提出了一种基于局部相对形状上下文与Q-谱的点模式匹配算法,对每个点构造相应的线图,并对线图的无符号Laplacian矩阵进行谱分解;利用谱分解所获得的特征值(Q-谱)作为点的特征,进而计算点的初始匹配概率;通过定义局部相对形状上下文计算点的相似性距离;将Q-谱方法与局部相对形状上下文结合进行概率松弛迭代获得匹配结果.实验结果表明了本文算法的可行性与有效性.     

基于特征点的快速匹配算法

为了实现快速、高精度的图像匹配,将基于灰度的图像匹配技术与基于特征的图像匹配技术相结合.定义了一种灰度值特征.并提出一种基于图像灰度值特征点的快速匹配搜索算法.该算法利用得出的灰度值特征点作为搜索依据,改变了传统匹配方法遍历性质的搜索策略.由于特征点的选取对噪声和灰度值的线性变化具有一定的"免疫性".因此对灰度值不均匀的图像匹配问题同样适用.该算法在大大提高匹配速度的同时兼顾了匹配精度,实现了高效率、高精度的图像匹配.通过实验,证实了该算法的快速性和准确性.     

Some Equivalences between Kernel Methods and Information Theoretic Methods

In this paper, we discuss some equivalences between two recently introduced statistical learning schemes, namely Mercer kernel methods and information theoretic methods. We show that Parzen window-based estimators for some information theoretic cost functions are also cost functions in a corresponding Mercer kernel space. The Mercer kernel is directly related to the Parzen window. Furthermore, we analyze a classification rule based on an information theoretic criterion, and show that this corresponds to a linear classifier in the kernel space. By introducing a weighted Parzen window density estimator, we also formulate the support vector machine in this information theoretic perspective.

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Feature extraction and recognition for echoes of HRR radar

This paper describes a novel target recognition scheme using High Range Resolution (HRR) ra-dar signatures. AutoRegressive (AR) method is used to extract features from HRR radar echoes based on scattering center model of target. The optimal linear transformation based on Euclidian distribution distance criterion is performed on AR model parameter vectors to reduce dimension of feature vectors further and improve the class discrimination capability of feature vectors. The optimization algorithm is designed utiliz-ing the quadratic property of criterion function and Gaussian kernel based Parzen window density function estimator. The concept of Stochastic Information Gradient (SIG) is incorporated into the gradient of cost function to decrease the computational complexity of the algorithm. Simulation results using three real air-planes, data show the effectiveness of the proposed method.     

谱线辨识方法

谱线辨识是数字信号处理的一个重要课题,利用完全对称窗的实谱特性可以判定分立谱线的存在;相继两次ＤＦＴ分析（实为一次复时域信号分析）可精确求出诸分立谱线的频率、幅度与相位。本文提出的分立谱线辨识方法普遍用于各种周期性时域信号的精密谱分析工作。     

On Detecting White Space Spectra for Spectral Scavenging in Cognitive Radios

A primary task performed by a Cognitive Radio is that of spectral estimation to locate the segments in a spectral span that contain white zones, spans that contains noise only, or grey zones, spans that contain signals with significant intervals of off-time. The identification of spectral regions containing noise only spectra, as opposed to regions containing signals with low spectral density, is surprising difficult. The estimator must deal with questions of transform length, window selection, window length, window overlap, and ensemble averaging options. This paper describes the impact of each selection option and presents the architecture of the optimal spectral estimator.

Mahalanobis Distance Based Approach for Anomaly Detection of Analog Filters Using Frequency Features and Parzen Window Density Estimation

Analog filters play a very important role in insuring the availability of electronic systems. Early detection of anomalies of analog filters can prevent the impending failures and enhance reliability. The complex architecture and the tolerances of multiple components make it very difficult to detect anomalies in analog filters. To address this concern, A Mahalanobis distance (MD) based anomaly detection method for analog filters is proposed in this paper. The conventional frequency features and the moment of frequency response are selected as the feature vector. Mahalanobis distance is used to transform the frequency feature vector to one dimensional MD data. The anomaly detection threshold is obtained based on probability density of the health MD data sets which is estimated by Parzen window density estimation method. The efficiency of the proposed method has been verified by two case studies. In the case studies, a comprehensive indicator constructed by miss alarm and false alarm is used to obtain an optimal anomaly detection threshold. One class SVM (OCSVM) based anomaly detection method is used as a comparison with our approach. The results illustrate that: (1) the proposed frequency features can effectively clarify the degradation of analog filters; (2) the proposed MD based approach can detect anomalies in analog filters effectively at an early time stage. (3) the proposed MD based approach can detect anomalies in analog filters more accurately than OCSVM based method.     

Minimum variance filters and mixed spectrum estimation

This paper presents a spectral density estimator based on a normalized minimum variance (MV) estimator as the one proposed by Lagunas. With an equivalent frequency resolution, this new estimator preserves the amplitude estimation lost in Lagunas one. This proposition comes from a theoretical study of MV filters that highlights this amplitude lost. Two signal types are taken into account: periodic deterministic signals (narrow-band spectral structures) and stationary random signals (broad-band spectral structures). Without selecting a smoothing window, the proposed estimator is an alternative to Fourier-based estimator and, without modeling the signal, it is a concurrent to high-resolution estimators.     

Efficient selection of discriminative genes from microarray gene expression data for cancer diagnosis

A new mutual information (MI)-based feature-selection method to solve the so-called large p and small n problem experienced in a microarray gene expression-based data is presented. First, a grid-based feature clustering algorithm is introduced to eliminate redundant features. A huge gene set is then greatly reduced in a very efficient way. As a result, the computational efficiency of the whole feature-selection process is substantially enhanced. Second, MI is directly estimated using quadratic MI together with Parzen window density estimators. This approach is able to deliver reliable results even when only a small pattern set is available. Also, a new MI-based criterion is proposed to avoid the highly redundant selection results in a systematic way. At last, attributed to the direct estimation of MI, the appropriate selected feature subsets can be reasonably determined.     

一种多光谱红外舰船目标融合检测方法

针对复杂岸岛背景下的红外舰船目标检测问题,提出了一种多光谱融合红外舰船目标检测方法。首先根据不同谱段信息相互间的关系进行基于非下采样轮廓波变换(Nonsubsampled Contourlet Transform, NSCT)域的多级多光谱图像融合,然后利用LSD线段检测和聚类对融合后的图像进行岸岛线检测。采用选择性搜索算法生成初始目标候选区域,然后结合岸岛线空间位置以及舰船目标的几何特征和灰度特征约束剔除部分虚假目标区域,最后提取候选区域的方向梯度直方图(Histogram of Oriented Gradient, HOG)特征算子。利用线性支持向量机(Support Vector Machine, SVM)分类器进行分类识别,以检测出真实舰船目标。实验结果表明,与单谱段红外舰船目标检测方法相比,本文方法在检测精度上有较大提升。