在未知瞬态信号检测中，一般采用广义似然比检测。
To detect unknown signals, we usually use the generalized likelihood ration test (GLRT).
本文提出了一种利用广义似然比检测图象边缘的算法。
In this paper, We present an edge detection algorithm using generalized likelihood ratio.
然而广义似然比检测不能有效利用瞬态信号的固有特性，检测性能不是很好。
However, the performance of the GLRT is not very good since it does not adequately use the intrinsic characteristic of transients.
为了进一步从通过相关检测门限的像素中检测出目标，又提出了一种新的广义似然比检测方法。
In order to further the targets detection in these few pixels, an improved GLRT method is developed.
利用广义似然比检验（GLRT）解码算法，导出了成对符号错误概率（PEP）上限和码的设计准则。
The chernoff upper bound of the pairwise error probability(PEP) and the code design criteria are derived by means of the generalized likelihood-ratio test(GLRT) decoding algorithm.
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。
Then based on the temporal difference models, we formulate the detection problem in 2 steps, correlation detection and generalized likelihood ratio test (GLRT).
分组判决算法利用检测窗内所有数据帧，在广义似然比准则下对相关模板进行联合估计，进一步压制了模板噪声。
The group decision algorithm based on GLRT principle was utilized to estimate correlation template through all data frames in detection window and could offer better noise suppression.
该方法首先对各距离分辨单元在方位向进行非相参积累，然后利用类似单个脉冲下扩展目标的广义似然比检测器来实现高分辨率雷达的检测。
First, each range resolving unit was accumulated non-inherently in azimuth, and then the detection of HRR target was done with the GLRT detector similar to that of the mono-pulse spread targets.
讨论矩约束条件下的广义经验似然比统计量族以及相应的性质。
The family of generalized empirical likelihood ratio statistics with moment restrictions, which is a generalization of Baggerly, is investigated.
讨论矩约束条件下的广义经验似然比统计量族以及相应的性质。
The family of generalized empirical likelihood ratio statistics with moment restrictions, which is a generalization of Baggerly, is investigated.
应用推荐