The final target representation model was obtained by means of linear fusing the two feature models, and the fusion coefficient was determined adaptively by contrast ratio of feature likelihood map.
并将两种特征模型进行线性融合，得到最终的目标表征模型，其中的融合系数由特征似然图对比度自适应确定。
After analyzing the feature indexes of air targets, a target identification model based on grey relation is presented.
在分析研究空中目标特征指标的基础上，提出并建立了基于灰色关联的目标识别模型。
The target recognition of is processed by the feature of the sea battlefield's multi-sensors. In this way, a pertinent arithmetic model is formed.
依据海战场多传感器探测的目标特点对多传感器目标进行识别处理，形成一个具有针对性的识别算法模型。
Methods of feature extraction and classification of target in model-based SAR ATR using feature are studied systemically in this paper.
本文系统研究了利用特征基于模型SARA TR系统中，SAR图像目标特征提取方法和分类方法。
Feature interest measure is introduced in the hybrid user model, which can reflect the degree of feature preference of users and obtain more accurate similarity between target user and the neighbors.
混合用户模型引入的特征兴趣度，反映了用户对特征的偏好程度，在计算用户之间相似度时更为准确。
Based on the study of the influence of the quantification noise and the gray level noise on target feature image, the error model of the target feature image centroid coordinates is built.
深入研究了灰度噪声、量化噪声对目标特征图像的影响及其误差模型，并由此建立了目标特征图像质心坐标提取误差模型。利用仿真试验分析了灰度噪声、量化噪声导致的航天器间相对状态参数测量的误差分布。
Based on the study of the influence of the quantification noise and the gray level noise on target feature image, the error model of the target feature image centroid coordinates is built.
深入研究了灰度噪声、量化噪声对目标特征图像的影响及其误差模型，并由此建立了目标特征图像质心坐标提取误差模型。利用仿真试验分析了灰度噪声、量化噪声导致的航天器间相对状态参数测量的误差分布。
应用推荐