一种利用目标雷达高分辨距离像幅度起伏特性的特征提取新方法

为了松弛高分辨距离像(HRRP)的方位敏感性,传统的雷达HRRP目标识别方法大都采用目标在一定方位角域内的平均像作为方位模板.实际上,距离像的幅度起伏特性也包含了一定的目标特征信息.本文基于散射点模型理论,提出了一种利用距离像幅度起伏特性的特征提取新方法.新方法提取的加权距离像特征反映了各个距离单元内目标散射点的分布情况,可以更好地描述目标散射特性.基于外场实测数据的识别实验结果表明,新的特征提取方法可以大幅度地提高识别性能.     

Automatic target recognition of radar HRRP based on high order central moments features

The paper addresses the problem of target recognition using High-resolution Radar Range Profiles (HRRP). A novel approach of feature extraction and dimension reduction based on extended high order central moments is proposed in order to reduce the dimension of range profiles. Features extracted from radar HRRPs are normalized and smoothed, and then comparative analysis of the similar approaches is done. The range profiles are obtained by step frequency technique using the two-dimensional backscatters distribution data of four different aircraft models. The template matching method by nearest neighbor rules, which is based on the theory of kernel methods for pattern analysis, is used to classify and identify the range profiles from four different aircrafts. Numerical simulation results show that the proposed approach can achieve good performance of stability, shift independence and higher recognition rate. It is helpful for real-time identification and the engineering implements of automatic target recognition using HRRP. The number of required templates could be reduced considerably while maintaining an equivalent recognition rate.     

基于一维距离像序列的弹道目标融合识别研究*

一维距离像是宽带雷达目标识别的重要特征之一.本文根据弹道目标的微动特性,推导了微动弹道目标的时间-距离像模型.然后提取具有平移不变性的中心矩和双谱作为待识别特征向量,并分别使用K-L变换和局部双谱法对提取到的中心矩和双谱特征进行降维.将降维后的特征分别输入支持向量机进行分类识别,最后将支持向量机的输出进行决策级融合,得到待识别目标的识别概率.与基于单特征量的识别方法相比,本文提出的方法不仅具有较高的识别率,而且具有良好的抗噪能力.     

Radar HRRP recognition based on the minimum Kullback-Leibler Distance criterion

To relax the target aspect sensitivity and use more statistical information of the High Range Resolution Profiles （HRRPs）, in this paper, the average range profile and the variance range profile are extracted together as the feature vectors for both training data and test data representation. And a decision rule is established for Automatic Target Recognition （ATR） based on the minimum Kullback-Leibler Distance （KLD） criterion. The recognition performance of the proposed method is comparable with that of Adaptive Gaussian Classifier （AGC） with multiple test HRRPs, but the proposed method is much more computational efficient. Experimental results based on the measured data show that the minimum KLD classifier is effective.     

基于部分标记数据进行人脸图像特征提取

针对无监督特征提取的识别率低与监督特征提取需要大量标记的问题,提出一种基于部分标记数据的半监督判别分析(SSDPA)特征提取法。本文方法能实现图像数据降维,避免线性判别分析(LDA)存在的小样本问题,达到提高识别率的目的。算法对图像进行离散余弦变换(DCT)变换;根据DCT图像的频率分布,利用部分标记数据计算SSDP;优先搜索SSDP高的DCT图像信息。将本文方法与其它方法进行组合,在不同人脸数据库上进行了实验。实验证明了本文方法的有效性,用较低的代价获得了优于传统方法的识别率。     

雷达高分辨距离像自动目标识别方法的改进

在雷达自动目标识别中,广泛利用基于散射点模型的高分辨距离像(HRRP),并取得较好的识别效果。由于散射点具有一些特点,且距离单元内的散射点的情况有时比较复杂,从而使高分辨距离像出现一些异常,导致识别发生误判。该文针对发生的问题,主要讨论了飞机类目标对偏航、俯仰、侧摆三维姿态角变化的敏感性、飞机类目标在正侧视附近的特点以及测试样本的相干峰现象,并提出了相应的改进措施。仿真数据的识别试验结果表明该文提出的改进措施可以有效地提高识别性能。     

Singular Value Decomposition Projection for solving the small sample size problem in face recognition

Numerous dimensionality reduction methods have achieved impressive performance in face recognition field due to their potential to exploit the intrinsic structure of images and to enhance the computational efficiency. However, the FR methods based on the existing dimensionality reduction often suffer from small sample size (SSS) problems, where the sample dimensionality is larger than the number of training samples per subject. In recent years, Sparse Representation based Classification (SRC) has been demonstrated to be a powerful framework for robust FR. In this paper, a novel unsupervised dimensionality reduction algorithm, called Singular Value Decomposition Projection (SVDP), is proposed to better fit SRC for handling the SSS problems in FR. In SVDP, a weighted linear transformation matrix is derived from the original data matrix via Singular Value Decomposition. The projection obtained in this way is row-orthonormal and it has some good properties. It makes the solution be robust to small perturbations contained in the data and has better ability to represent various signals. Thus, SVDP could better preserve the discriminant information of the data. Based on SVDP, a novel face recognition method SVDP-SRC is designed to enable SRC to achieve better performance via low-dimensional representation of faces. The experiments carried out with some simulated data show that SVDP achieves higher recovery accuracy than several other dimensionality reduction methods. Moreover, the results obtained on three standard face databases demonstrate that SVDP-SRC is quite effective to handle the SSS problems in terms of recognition accuracy.     

基于纹理与特征选择的前视红外目标识别

针对前视红外(FLIR)目标自动目标识别(ATR)问题 ,提出了一种基于纹理特征的ATR方法。不同于传统基于学习、基于 模板以及基于稀疏表示的方法,从图像灰度入手,提出采用局部三值模式(LTP )描述图像纹理特征,同时结合FLIR图像的特点,对LTP进行了增强处 理;然后针对特征的高维问题,采用特征选择方法进行降维处理;最后 采用降维后的特征实现ATR。实验结果表明,本文方法取得了比传统方法 更好的效果;同时也证明,仅从纹理分析入手,也能较好地实现前视红外目标的ATR。     

基于高分辨距离像序列的锥柱体目标进动和结构参数估计

 弹道目标特征参数估计是进行目标识别的基础。针对缺少先验参数信息时锥柱组合类弹头目标进动和结构参数联合估计难题,该文提出一种基于高分辨距离像序列实现锥柱体目标进动和结构参数联合估计新方法。以旋转对称锥柱体目标为研究对象,基于静态电磁散射数据,结合目标运动模型仿真生成了目标高分辨距离像序列,分析了4个观测区域内锥柱体目标的1维距离像特性。研究了常见雷达观测视角内锥柱体各散射中心的1维距离像序列变化规律,建立了序列中散射中心间的相对位置变化的极值与目标参数之间的关系式,据此完成了锥柱体目标进动和结构参数的联合估计。最后,仿真实验结果验证了文中方法的有效性和适应性。     

基于一种改进的监督流形学习算法的语音情感识别

为了有效提高语音情感识别的性能,需要对嵌入在高维声学特征空间的非线性流形上的语音特征数据作非线性降维处理。监督局部线性嵌入(SLLE)是一种典型的用于非线性降维的监督流形学习算法。该文针对SLLE存在的缺陷,提出一种能够增强低维嵌入数据的判别力,具备最优泛化能力的改进SLLE算法。利用该算法对包含韵律和音质特征的48维语音情感特征数据进行非线性降维,提取低维嵌入判别特征用于生气、高兴、悲伤和中性4类情感的识别。在自然情感语音数据库的实验结果表明,该算法仅利用较少的9维嵌入特征就取得了90.78%的最高正确识别率,比SLLE提高了15.65%。可见,该算法用于语音情感特征数据的非线性降维,可以较好地改善语音情感识别结果。     

基于正则化零空间鉴别分析的人脸识别

提取有效的特征对高维数据的模式分类起着关键 作用,针对现有故障特征维数过高的问题,本文提 出了一种基于正则化零空间线性鉴别分析(Exponential Regularized Null Space Linear Discriminant Analysis, ERNSLDA)的特征提取方法。零空间线性判别分析已经在数据降维和特征提取上展现出良好 的性能,在 本文中,首先对类内样本矩阵进行正则化处理,避免小样本问题,其次对判别准则进行指数 化处理。所提 方法集成了NSLDA和RLDA在模式识别上的优势,有效地提高了人脸识别的精度,在ORL和YALE 数据库上的仿真实验证了本文所提方法的有效性。     

基于主成分分析和模糊识别方法的生物分子太赫兹光谱识别

提出了一种基于主成分分析（PCA）和模糊模式识别方法的生物分子太赫兹（THz）光谱识别方法,并采用多种典型糖类和氨基酸生物分子的太赫兹透射光谱作为实验介质证明所提方法的可行性和有效性。首先,运用PCA方法对生物分子太赫兹光谱数据做降维处理,提取样品太赫兹光谱特征信息;然后,用获得的主成分得分矩阵代替原始太赫兹光谱数据输入到模糊模式识别分析模型中,运用基于择近原则的模糊模式识别方法对待定样品进行分类识别。实验结果表明以生物分子的太赫兹光谱作为数据特征,采用PCA与模糊识别相结合的方法实现生物分子的检测和识别是可行的,该方法为太赫兹光谱技术用于生物分子的鉴定和识别提供了一种新的有效的分析方法。     

A Modified Minimum Classification Error (MCE) Training Algorithm for Dimensionality Reduction

Dimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not all the newly added features are helpful to classification. Therefore it is necessary to reduce the dimensionality of feature space for effective and efficient pattern recognition. Two popular methods for dimensionality reduction are Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). While these methods are effective, there exists an inconsistency between feature extraction and the classification objective. In this paper we use Minimum Classification Error (MCE) training algorithm for feature dimensionality reduction and classification on Daterding and GLASS databases. The results of MCE training algorithms are compared with those of LDA and PCA.     

基于核协同表示与鉴别投影的辐射源调制识别

针对辐射源识别中的特征稳定性不高和低信噪比环境适应性不足等问题,提出了一种基于二次时频分布、核协同表示与鉴别投影的识别方法.首先,通过时频变换、稀疏域降噪和二次特征提取的预处理算法降低噪声干扰和特征冗余,以获取高稳定性的二次时频分布特征;然后,采用核协同表示和鉴别投影思想进行降维学习和字典学习,以提升数据低维表征和类间鉴别能力;最后,通过离线训练完成系统优化并用于分类验证.仿真结果表明,二次时频分布特征具备较高稳定性,识别方法具备较强鲁棒性、时效性和适应性;当信噪比为-10dB时,该方法对8类辐射源信号的整体平均识别率达到96.88%.     

复杂场景下结合SIFT与核稀疏表示的交通目标分类识别

针对复杂场景下的交通目标分类识别难点,提出一种基于尺度不变特征转换(SIFT)与核稀疏表示的分类识别算法.该算法首先利用SIFT分别提取训练样本和待测目标局部特征信息,通过核方法将特征样本映射到核空间,构建过完备字典,最后通过待测目标在字典中的稀疏度与重构误差对交通目标类别进行判定.同时,分析了随机投影下的核稀疏表示分类与特征维数之间的关系.实验结果表明,与SVM、稀疏表示分类(SRC)相比,该方法增强了交通目标特征层的类判别能力,具有较好的识别率和鲁棒性.     

A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction

Canonical correlation analysis (CCA) is an efficient method for dimensionality reduction on two-view data. However, as an unsupervised learning method, CCA cannot utilize partly given label information in multi-view semi-supervised scenarios. In this paper, we propose a novel two-view semi-supervised learning method, called semi-supervised canonical correlation analysis based on label propagation (LPbSCCA). LPbSCCA incorporates a new sparse representation based label propagation algorithm to infer label information for unlabeled data. Specifically, it firstly constructs dictionaries consisting of all labeled samples; and then obtains reconstruction coefficients of unlabeled samples using sparse representation technique; at last, by combining given labels of labeled samples, estimates label information for unlabeled ones. After that, it constructs soft label matrices of all samples and probabilistic within-class scatter matrices in each view. Finally, in order to enhance discriminative power of features, it is formulated to maximize the correlations between samples of the same class from cross views, while minimizing within-class variations in the low-dimensional feature space of each view simultaneously. Furthermore, we also extend a general model called LPbSMCCA to handle data from multiple (more than two) views. Extensive experimental results from several well-known datasets demonstrate that the proposed methods can achieve better recognition performances and robustness than existing related methods.     

基于多线性核主成分分析的掌纹识别

提出运用多线性核主成分分析(MKPCA)的一种新方法进行掌纹识别.首先MKPCA通过非线性变换,将输入样本图像向高维特征空间F上投影,运用多线性主成分分析(MPCA)直接对掌纹张量进行降维,得到低维的投影张量;然后掌纹图像向张量子空间上投影提取特征向量;最后计算特征向量间的余弦距离进行掌纹匹配.运用PolyU掌纹图像库...     

基于KPCA和类峰值特征的模拟电路诊断方法

针对传统的核主成分分析方法(KPCA)无法解决在故障样本交叠严重时多分类性能较差的问题,提出一种基于改进KPCA的特征提取和类峰值特征辅助识别分类相结合的模拟电路故障诊断方法.在预处理阶段,提出了一种图像混合欧氏距离用于建立核函数,进行核主成分分析特征提取,克服了传统KPCA的局限性;并且设计了一种用类峰值特征识别的方...     

基于核主成分分析的二次雷达脉冲信号初始特征提取

针对二次雷达脉冲信号的特征选择与分类问题进行研究,提出了一种基于核主成分分析(KPCA)的初始特征提取方法.根据二次雷达脉冲信号的特点,首先经过数据整编、预处理,获取样本的初始特征参数;然后利用KPCA方法对特征参数进行主成分组合,以消除信号特征间的相关性和压缩特征向量的维数,最后利用聚类工具进行分类.数学分析和可视化实验结果都表明这种分析方法是有效的.试验还表明,KPCA在特征选取方面性能优于PCA.     

An improved traffic flow forecasting based control logic using parametrical doped learning and truncated dual flow optimization model

Traffic data-based object position forecasting is an exciting research area in feature recognition and analytic data systems. This recognition system is deployed in many applications to ensure forecasting, position identification, and prediction accuracy. Several methods of data processing techniques are available in the existing works, but it limits the issues like reduced accuracy, classification efficiency, and increased error rate. This paper intends to develop a new pattern extraction-based classification technique for traffic feature recognition to solve these problems. At first, the input test data is preprocessed by arranging the users with a proper cluster index for representing the data matrix with the signal pattern as in the data table, which is considered a feature matrix for the classifier. After that, the block separation is performed to extract the most valuable patterns from the optimized data by using Truncated Dual Flow Optimization (TDFO) using a Parametrical Doped Learning (PDL) based classifier to recognize the information on the extracted feature vectors. During experimental analysis, the performance of the proposed technique is validated by using various evaluation measures. Also, it is compared with some other existing methods for proving the superiority of the proposed pattern extraction-based classification system.