1) Likelihood Ratio Tes(tLRT)
似然比测试
1.
Voice activity detection algorithm based on Likelihood Ratio Tes(tLRT) using the principle of hypothesis testing,intro- ducing the reduction of noise,has good noise robustness,and it is efficient and easy to achieve.
基于似然比测试的语音激活检测算法基于假设检验原理,引入了对噪声的降噪处理,具有良好的噪声鲁棒性,算法高效且易于实现。
2) likelihood ratio test
似然比检测
1.
According to the model,a generalized likelihood ratio test rule was developed for the target identification,and then the analytical expression to calculate the target identification performance was theoretically given.
为了实现发射周期内目标的实时识别,首先建立了E脉冲激励下目标回波的二元假设检验模型,在此基础上导出了识别目标的广义似然比检测量,并从理论上给出了计算目标识别概率的解析表达式。
2.
First pretreat image through high pass or morphologic filtering, then progress likelihood ratio test to segment possible targets from the image.
首先通过高通或形态学滤波进行图像预处理 ,进一步用似然比检测分割出候选目标 ,考虑到环境干扰造成的目标在某一帧暂时消失的情况 ,提出了利用目标运动特征通过选择合适的邻域判决条件并结合图像流分析提取运动弱小目标的一种方法。
3.
A quasi-hybrid likelihood ratio test (qHLRT) classifier is proposed for linear modulation classification,with unknown carrier frequency offsets (CFO).
针对存在未知载波频偏(CFO)的线性调制分类,提出一种混合似似然比检测(qHLRT)分类器。
3) detection likelihood ratio
检测似然比
1.
Based on the characteristics of the detection likelihood ratio sequence and the recursive relations,a fast algorithm is proposed.
文中根据检测似然比序列的特点,利用递推关系,提出了一种快速算法。
4) GLRT
广义似然比检测
1.
Based on the temporal difference models for background noise pixel,target pixel and clutter pixel,we formulate the detection problem in 2 steps,correlation detection and generalized likelihood ratio test(GLRT).
在图像序列中背景像素、目标像素以及杂波像素的时域差分模型基础上提出了红外小目标时域检测算法,算法共分为两步:相关检测和广义似然比检测。
2.
Then based on the temporal difference models, we formulate the detection problem in 2 steps, correlation detection and generalized likelihood ratio test (GLRT).
然后基于时域差分模型提出了红外慢速小目标时域检测算法，算法共分为两步：相关检测和广义似然比检测。
5) maximum likelihood ratio detection(MLD)
极大似然比检测
1.
A sub-carrier allocation algorithm based on maximum likelihood ratio detection(MLD) in cognitive OFDM is introduced and studied.
研究了认知OFDM中基于极大似然比检测(MLD)的子载波分配算法,认知用户采用MLD模型对主用户频谱使用情况进行分布式检测,利用频谱检测信息动态分配子载波,通过认知基站对认知用户子载波频谱感知信息进行融合判决。
6) Likelihood ratio
似然比
1.
Multi-dimensional correlation test based on the probability integral translation and likelihood ratio
基于概率积分变换与似然比的高维相关性检验
2.
Discrete calculation of signal to noise ratio and likelihood ratio are discussed in detail,and one practical algorithm for the digital implementation of RPPT Detector is presented.
文章提出了高分辨率雷达目标随机参量脉冲串检测器的数字实现方法;详细讨论了离散条件下信噪比与似然比的计算,给出了RPPT检测器数字实现的一种实用算法。
3.
By making use of the notion of likelihood ratio and the approach of Laplace trans- form,a class of strong limit theorems represented by inequalities which call the strong deviation theorems are obtained.
研究了相依连续型非负随机变量序列的极限性质,利用似然比的概念和Laplace变换方法得到了一类强偏差定理,即用不等式表示的一类强极限定理。
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