样本加权约束能量最小化算法

 针对高光谱图像小目标探测中约束能量最小化算法对同类地物光谱多样性敏感,且不能有效识别大目标的问题,提出了一种样本加权CEM目标探测算法.通过光谱单位化处理,减小了目标点所在环境不同而出现的光谱差异.为精确地确定目标物在所有像元中所占的比例,以光谱相关性作为权值的度量对样本进行加权处理,有效降低了目标像素在样本自相关矩阵运算中所占的比重,使算法对大目标探测同样有效.     

非高斯非平稳背景的红外弱小目标检测

文中利用最小二乘估计对每一像素邻域的相关长度取最优值,并依据相关长度对相关 矩阵采取了一种新的处理方法,解决了非平稳图像像素邻域矢量间的相关矩阵不可逆的问题,改进了Chapp le和Bertilone构造的基于高斯模型的弱小目标检测方法,从而得出了一种新的红外目标检测算法,适用于非高斯非平稳背景的红外弱小目标检测.该目标检测算法首先采用一个转化函数将图像高斯化,然后求得可逆的相关矩阵,接着求像素邻域矢量的概率密度,最后依据概率密度确定阈值,提取弱小目标。实验结果证实了文中算法的可靠性和可行性。     

一种MOE盲自适应空时多用户检测算法的性能分析

将相关矩阵多次幂技术与MOE(最小输出能量)盲自适应空时多用户检测相结合提出了一种新的空时多用户检测算法。并且,从噪声方差、采样数目和u次幂的角度,对这种改进的MOE盲空时多用户检测接收机的性能进行了分析。理论证明和仿真结果表明,这种新的检测技术在噪声方差非常小、采样数目趋于无穷以及u→∞的情况下,它的性能收敛于MMSE盲自适应空时多用户检测。     

基于RX算法的高光谱红外弱小目标检测

主要针对高光谱红外图像中弱小目标的检测方法进行研究,根据目标检测的RX算法模型,给出了算法在高光谱目标检测中的性能评价,分析了检测器像元数、光谱波段数对算法性能的影响,在RX算法及相关改进算法的基础上,通过对AVIRIS数据的仿真实验,分析了相关矩阵在高光谱目标检测中算法的应用优势,提出了一种目标自动检测方法,验证了UTD算法对有特定波段依赖性的小目标有良好检测效果.理论分析和仿真实验证明了RX算法在红外高光谱目标自动检测中的有效性.     

基于IMBFO算法的干扰资源优化

传统的战场决策多依赖于人工经验进行实施,随着实际战争电磁环境的复杂性和恶劣性逐渐增长,传统决策方法略显不足.针对此问题,将干扰资源的优化决策问题建模为最小化干扰功率、最小化系统效能匹配度、最大化压制概率的多目标函数约束模型,通过对传统多目标细菌觅食优化(MBFO)算法进行改进,利用改进多目标细菌觅食优化(IMBFO)算法对问题进行求解,使得决策方案更加科学、合理.仿真结果表明,IMBFO算法能够有效求解该问题,并与MBFO算法、带有精英保留策略的快速非支配多目标优化算法(NSGA-Ⅱ)相比,具有良好的分布性和收敛性.     

频域和时域最小二乘法在声场重建中的性能比较

最小二乘法在声场重建中应用广泛,主要分为频域和时域两种实现形式.频域最小二乘法是对各频点上的声场重建误差进行最小化控制,通常需要在各频点进行独立的正则化处理,导致重建滤波器的非因果性,在实际应用中需要引入时延因子.时域最小二乘法是对控制点上的时域重建误差进行最小化控制,对重建滤波器系数进行整体求解,不存在非因果问题.本文利用两种算法仿真了自由场假设下的单目标区域和多目标区域的声场重建问题,仿真结果表明,在滤波器阶数相同的情况下,两种算法在单目标区域声场重建问题中表现相近,在多目标区域的声场重建问题中,时域算法明显优于频域算法.     

基于端元提取的超光谱图像目标检测算法

针对超光谱图像中目标检测问题,提出了一种基于端元提取的超光谱图像目标检测算法。该算法在未知任何先验信息条件下,对超光谱数据进行基于噪声调节的主成分分析,通过保留信噪比较大的主成分,有效降低超光谱图像中的噪声水平;然后利用基于无监督正交子空间投影的端元提取算法获取图像中的端元矢量,将各端元矢量代入改进的约束能量最小化算子中,从而实现超光谱图像的目标检测。实验结果表明,该算法不但可以得到较好的目标检测结果,在运算性能上也具有一定的优势。     

子空间约束的最小二乘常模算法

常模算法(CMA)是一种性能优良的盲多用户检测算法.最小二乘常模算法(LSCMA)因其全局收敛性和稳定性而备受关注,但是在信噪比低时性能不理想.本文将最小二乘常模算法,紧缩近似投影子空间(PASTd)算法和奇异值分解(SVD)相结合,提出了一种子空间约束的最小二乘常模算法,称为SUB_LSCMA,其复杂度比已有的基于直接对接收信号自相关矩阵做特征值分解(ED)的LSCMA_SUB[6]复杂度低.仿真结果表明这种算法的收敛速度、跟踪性能和误码性能和LSC-MA_SUB基本相同.     

数据驱动二次相关滤波器红外目标检测

针对传统红外目标检测算法易受目标和背景先验样本质量、目标姿态和视角及噪声等的影响,提出了一种新的基于稀疏编码的数据驱动二次相关滤波器目标检测算法,其中给出了目标自相关矩阵基字典的概念,该数据驱动滤波器模型能包容多种姿态和视角的目标,并能抑制噪声和样本质量的影响,同时可以舍弃对无规律背景样本的依赖,通过对行人和车辆的实验验证了该算法的有效性.所提算法的设计思想对诸多滤波器算法的改进具有很好的借鉴意义.     

基于重排频谱时频脊的海面小目标检测

针对由于传统W-H算法计算量大,检测效率不高,海面小目标检测难度较大的问题,提出了基于重排频谱时频脊的小目标检测新算法.通过研究实测数据的时频谱能量分布特点,对比了在不同极化条件下,传统算法与新算法对多组实测数据的分析结果,研究了Hough参数域内的尖峰特性,对小目标实现了有效检测,验证了算法的可行性.最终得出结论:新算法选取重排算法以及提取重排谱时频脊提高了检测能力,降低了运算量,对海面小目标实现有效检测,且HH极化条件下新算法的检测性能更好.     

乳腺癌检测的三维时域微波断层成像方法

应用泛函分析和变分法,改进拉格朗日(Lagrange)乘子算法为一种三维时域微波断层成像方法,用于检测早期乳腺癌。该方法首先以最小二乘准则构造目标函数,将反演问题表示为约束最小化问题;接着应用罚函数法转化为无约束最小化问题;然后基于变分计算导出闭式的拉格朗日函数关于相对介电常数和电导率的Fréchet导数;最后借助梯度算法和时域有限差分(FDTD)法迭代求解。为了对抗噪声污染和逆问题的病态特性,采用了一阶的吉洪诺夫(Tikhonov)正则化方法。利用FDTD和PRP共轭梯度(CG)法,对三维半球乳房模型进行了仿真计算,仿真结果显示了方法的可行性。     

Constrained subpixel target detection for remotely sensed imagery

Target detection in remotely sensed images can be conducted spatially, spectrally or both. The difficulty of detecting targets in remotely sensed images with spatial image analysis arises from the fact that the ground sampling distance is generally larger than the size of targets of interest in which case targets are embedded in a single pixel and cannot be detected spatially. Under this circumstance target detection must be carried out at subpixel level and spectral analysis offers a valuable alternative. In this paper, the problem of subpixel spectral detection of targets in remote sensing images is considered, where two constrained target detection approaches are studied and compared. One is a target abundance-constrained approach, referred to as nonnegatively constrained least squares (NCLS) method. It is a constrained least squares spectral mixture analysis method which implements a nonnegativity constraint on the abundance fractions of targets of interest. Another is a target signature-constrained approach, called constrained energy minimization (CEM) method. It constrains the desired target signature with a specific gain while minimizing effects caused by other unknown signatures. A quantitative study is conducted to analyze the advantages and disadvantages of both methods. Some suggestions are further proposed to mitigate their disadvantages     

Orthogonal eigensubspace estimation using neural networks

We present a neural network (NN) approach for simultaneously estimating all or some of the orthogonal eigenvectors of a symmetric nonindefinite matrix corresponding to its repeated minimum (in magnitude) eigenvalue. This problem has its origin in the constrained minimization framework and has extensive applications in signal processing. We recast this problem into the NN framework by constructing an appropriate energy function which the NN minimizes. The NN is of feedback type with the neurons having sigmoidal activation function. The proposed approach is analyzed to characterize the nature of the minimizers:of the energy function. The main result is that “the matrix W* is a minimizer of the energy function if and only if the columns of W* are the orthogonal eigenvectors with a given norm corresponding to the smallest eigenvalue of the given matrix”. Further, all minimizers are global minimizers. Bounds on the integration time-step that is required to numerically solve the system of differential equations (which define the dynamics of the NN) have also been derived. Results of computer simulations are presented to support our analysis     

一种基于神经网络的多层通孔最小化方法

在多层布线的线段－相交图模型基础上，利用Ｈｏｐｆｉｅｌｄ人工神经网络理论，通过反通孔数目这个优化目标与Ｈｏｐｆｉｅｌ网络能量函烽相联系的方法来解决多层布线通孔最小化问题。算法考虑了许多来自实际的约束。     

Development and analysis of a neural network approach toPisarenko's harmonic retrieval method

Pisarenko's harmonic retrieval (PHR) method is perhaps the first eigenstructure based spectral estimation technique. The basic step in this method is the computation of eigenvector corresponding to the minimum eigenvalue of the autocorrelation matrix of the underlying data. The authors recast a known constrained minimization formulation for obtaining this eigenvector into the neural network (NN) framework. Using the penalty function approach, they develop an appropriate energy function for the NN. This NN is of feedback type with the neurons having sigmoidal activation function. Analysis of the proposed approach shows that the required eigenvector is a minimizer (with a given norm) of this energy function. Further, all its minimizers are global minimizers. Bounds on the integration time step that is required to numerically solve the system of nonlinear differential equations, which define the network dynamics, have been derived. Results of computer simulations are presented to support their analysis     

Real-time processing algorithms for target detection andclassification in hyperspectral imagery

The authors present a linearly constrained minimum variance (TCMV) beamforming approach to real time processing algorithms for target detection and classification in hyperspectral imagery. The only required knowledge for these LCMV-based algorithms is targets of interest. The idea is to design a finite impulse response (FIR) filter to pass through these targets using a set of linear constraints while also minimizing the variance resulting from unknown signal sources. Two particular LCMV-based target detectors, the constrained energy minimization (CEM) and the target-constrained interference-minimization filter (TCIMF), are presented. In order to expand the ability of the LCMV-based target detectors to classification, the LCMV approach is further generalized so that the targets can be detected and classified simultaneously. By taking advantage of the LCMV-based filter structure, the LCMV-based target detectors and classifiers can be implemented by a QR-decomposition and be processed line-by-line in real time. The experiments using HYDICE and AVIRIS data are conducted to demonstrate their real time implementation     

多元假设检验GMPHD轨迹跟踪

由于在军事和民事领域逐步广泛的应用,数目不定的多目标跟踪技术正受到越来越多的关注。概率假设密度(PHD)滤波方法,特别是具有闭式递归的高斯混合概率假设密度(GMPHD)技术,在噪声和漏警等影响下仍能形成优越的群目标跟踪性能。然而PHD滤波器并不能实现多目标航迹跟踪,而其与传统数据互联的结合,复杂度高且跟踪效果不尽如人意。在该文中,各目标的航迹信息以假设形式表述,数据互联则是通过使用经典的多元假设检测方法判决假设矩阵实现。其与GMPHD的结合不仅实现了数据互联和轨迹管理,还因为积累时间信息大大降低了杂波干扰的影响。实验结果证明,该算法可以对多个目标所形成的轨迹实施正确跟踪,同时,计算量的大幅度降低带来了跟踪系统可实现性的提高。     

Rank constrained temporal-spatial matrix filters for CDMA systems

Efficient interference suppression techniques are needed to maximally utilize the potential gains of code-division multiple-access systems. In this letter, a receiver structure which combines multiuser detection (temporal filtering) and receiver beamforming (spatial filtering) in a multipath environment is considered. Following previous work, we model the receiver as a linear matrix filter and use the minimum mean-squared error (MMSE) as the performance criterion. Motivated by the high complexity of the optimum receiver, we propose rank constrained temporal-spatial filters which are simpler and near-optimum. The MSE is minimized subject to a structural constraint, using an iterative algorithm based on alternating minimization. The constraint on the receiver matrix filter narrows down the solution space, which helps to solve the optimization problem more efficiently. The constraint can be set appropriately by the system designer to achieve the desired tradeoff between performance and complexity. Numerical results indicate that a performance close to that of the optimum filter can be achieved with a simple iterative structure, even in highly loaded systems. Adaptive implementation of the rank constrained filters is derived. A new adaptive scheme is proposed which is a combination of the alternating minimization and the least mean squares methods. The convergence properties are investigated along with the effect of the number paths.     

Detection of spectral signatures in multispectral MR images for classification

This paper presents a new spectral signature detection approach to magnetic resonance (MR) image classification. It is called constrained energy minimization (CEM) method, which is derived from the minimum variance distortionless response in passive sensor array processing. It considers a bank of spectral channels as an array of sensors where each spectral channel represents a sensor and object spectral signature in multispectral MR images are viewed as signals impinging upon the array. The strength of the CEM lies on its ability in detection of spectral signatures of interest without knowing image background. The detected spectral signatures are then used for classification. The CEM makes use of a finite impulse response (FIR) filter to linearly constrain a desired object while minimizing interfering effects caused by other unknown signal sources. Unlike most spatial-based classification techniques, the proposed CEM takes advantage of spectral characteristics to achieve object detection and classification. A series of experiments is conducted and compared with the commonly used c-means method for performance evaluation. The results show that the CEM method is a promising and effective spectral technique for MR image classification.     

Constrained band selection for hyperspectral imagery

Constrained energy minimization (CEM) has shown effective in hyperspectral target detection. It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper explores this idea for band selection and develops a new approach to band selection, referred to as constrained band selection (CBS) for hyperspectral imagery. It interprets a band image as a desired target signature vector while considering other band images as unknown signature vectors. As a result, the proposed CBS using the concept of the CEM to linearly constrain a band image, while also minimizing band correlation or dependence provided by other band images, is referred to as CEM-CBS. Four different criteria referred to as Band Correlation Minimization (BCM), Band Correlation Constraint (BCC), Band Dependence Constraint (BDC), and Band Dependence Minimization (BDM) are derived for CEM-CBS.. Since dimensionality resulting from conversion of a band image to a vector may be huge, the CEM-CBS is further reinterpreted as linearly constrained minimum variance (LCMV)-based CBS by constraining a band image as a matrix where the same four criteria, BCM, BCC, BDC, and BDM, can be also used for LCMV-CBS. In order to determine the number of bands required to select p, a recently developed concept, called virtual dimensionality, is used to estimate the p. Once the p is determined, a set of p desired bands can be selected by the CEM/LCMV-CBS. Finally, experiments are conducted to substantiate the proposed CEM/LCMV-CBS four criteria, BCM, BCC, BDC, and BDM, in comparison with variance-based band selection, information divergence-based band selection, and uniform band selection.