1) generalized multiresolution likelihood ratio(GMLR)
广义多分辨似然比
1.
We define a generalized multiresolution likelihood ratio(GMLR) which generalizes the classic likelihood ratio,and which,after identifying the difference between background and target for each of the multiresolutions of SAR imagery,sums up all the di.
首先在多分辨四叉树上定义了一个广义多分辨似然比,刻画并且累积了SAR(syntheticaperture radar)图像中目标与背景在不同分辨率上的差异,从而增大了目标与背景之间的区分度。
2) Generalized multiresolution likelihood ratio
广义多分辨似然比
1.
We present several new segmentation methods based on generalized multiresolution likelihood ratio(GMLR) and spatially variant mixture multiscale autoregressive(SVMMAR) model.
但是SAR图像斑点噪声的存在使得SAR图像分割方法对传统光学图像的分割方法提出严重的挑战,本文结合SAR图像的统计特征,主要提出了基于广义多分辨似然比(generalized multiresolution likelihood ratio简称GMLR)和空间变化的混合多尺度自回归(spatially variant mixture multiscale autoregressive简称SVMMAR)随机模型的SAR(synthetic aperture radar)图像分割方法。
4) GLRT
广义似然比
1.
Using the matched subspace method,the paper obtained the generalized likelihood ration detector(GLRT) and the probability of detection and false alarm.
针对在高斯广义平稳(W SS)有色噪声环境中含有未知参数信号的检测问题,对于广义似然比检验(GLRT)和匹配子空间方法,提出了一种新的检测算法。
2.
With the estimation of noise parameter model through the modified covariance method for the autoregressive model(AR),which has a higher accuracy than the periodogram method,then the generalized likelihood ratio tests(GLRT) are established.
然后利用渐进似然函数,建立了广义似然比(GLRT)检测器,该检测器的性能依赖于噪声的未知参数估计的精度,并且得到了检测概率和虚警概率的渐进表达式。
5) GLRT
广义似然比检验
1.
Having dyadic and recursive subdivision on image,having integral on Beamlet in each sub-square,the straight lines were approximated under GLRT and reconstructed.
利用一种Beamlet变换算法来提取遥感图像中的线性特征,通过对遥感图像按二进、递归进行划分,利用灰度信息,积分计算每一小块图像中的Beamlets,结合梯度信息,通过广义似然比检验GLRT(generalized likeli-hood ratio testing)来检测判断符合条件假设的Beamlets,重建线性目标。
2.
Having dyadic and recursive subdivision on image, having integral on beamlets in each sub-square, the straight lines are approximated under GLRT(Generalized Likelihood Ratio Testing) and reconstructed.
本文利用一种Beamlet变换算法来提取遥感图像中的直线特征,通过对遥感图像按二进、递归进行划分,利用灰度信息,积分计算每一小块图像中的Beamlets,结合梯度信息,通过广义似然比检验GLRT(Generalized Likelihood Ratio Testing)来检测判断符合条件假设的Beamlets,重建目标直线。
3.
Two Constant False Alarming Rate (CFAR) detection algorithms such as Generalized Likelihood Ratio Test (GLRT) and Adaptive Matched Filter (AMF) are studied and the intrinsic relationship between adaptive CFAR and Space Time Adaptive Processing (STAP) is revealed.
研究了广义似然比检验和自适应匹配滤波器两种恒虚警检测算法 ,揭示了它们与空时自适应处理之间的内在联系 。
6) generalized likelihood ratio test
广义似然比检验
1.
Aiming at the drawbacks of power inefficiency and high noise vulnerability in transmitted-reference ultra-wideband systems,a new auto-reference transmit-receive scheme and a corresponding group decision algorithm on the generalized likelihood ratio testing(GLRT) principle were proposed.
针对发射参考超宽带系统的低资源利用率和相关模板易受噪声干扰的缺点,提出一种新的自参考发射接收方案和基于此方案的广义似然比检验分组判决算法。
2.
Application of generalized likelihood ratio test to this model yielded maximum likelihood test statistics.
帧检测被等效为一个含未知参数(噪声功率、信道响应和载波频偏)的假设检验模型,对它应用广义似然比检验原理,导出了最大似然检测统计量,然后根据帧前导字周期性的特点进行简化,得到帧检测器。
3.
By applying generalized likelihood ratio test (GLRT), This paper gives the detector of polynomial phase signal (PPS) with unknown parameters.
根据Neyman-Pearson(NP)准则,利用广义似然比检验方法,给出了未知参数多项式相位信号的最优检测器。
补充资料：似然比检验
分子式：
CAS号：
性质：假设总体X是连续型的，其密度是p(x)，则x1，x2，…，xn，的联合密度为g(x1，x2,…,xn)= p(x1)。关于样本的密度函数g(Xl，X2，…Xn；θ)有两个假设，H0:g(x1，x2，…xn；θ0)=p(xi, θ0)和H1:g(x1,x2，…xn；θ1)=p (xi；θ1)。统计量L(X1，X2，…，Xn)=称为假设H0对H1的检验问题的似然比。以似然比作统计量的检验，称作似然比检验。
CAS号：
性质：假设总体X是连续型的，其密度是p(x)，则x1，x2，…，xn，的联合密度为g(x1，x2,…,xn)= p(x1)。关于样本的密度函数g(Xl，X2，…Xn；θ)有两个假设，H0:g(x1，x2，…xn；θ0)=p(xi, θ0)和H1:g(x1,x2，…xn；θ1)=p (xi；θ1)。统计量L(X1，X2，…，Xn)=称为假设H0对H1的检验问题的似然比。以似然比作统计量的检验，称作似然比检验。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条