Exo-atmospheric target discrimination using probabilistic neural network  

作　　者：王暕来 杨春玲 

机构地区：[1]School of Electrical Engineering and Automation, Harbin Institute of Technology

出　　处：《Chinese Optics Letters》2011年第7期1-5,共5页中国光学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (No. 60877065);the Research Fund for the Doctoral Program of Higher Education of China (No. 20092302110026);the Key Laboratory of All Optical Network and Advanced Telecommunication Network, Ministry of Education of China

摘　　要：Exo-atmospheric targets are especially difficult to distinguish using currently available techniques, because all target parts follow the same spatial trajectory. The feasibility of distinguishing multiple type compo- nents of exo-atmospheric targets is demonstrated by applying the probabilistic neural network. Differences in thermM behavior and time-varying signals of space-objects are analyzed during the selection of features used as inputs of the neural network. A novel multi-colorimetric technology is introduced to measure precisely the temporal evolutional characteristics of temperature and emissivity-area products. To test the effectiveness of the recognition algorithm, the results obtained from a set of synthetic multispectral data set are presented and discussed. These results indicate that the discrimination algorithm can obtain a remarkable success rate.Exo-atmospheric targets are especially difficult to distinguish using currently available techniques, because all target parts follow the same spatial trajectory. The feasibility of distinguishing multiple type compo- nents of exo-atmospheric targets is demonstrated by applying the probabilistic neural network. Differences in thermM behavior and time-varying signals of space-objects are analyzed during the selection of features used as inputs of the neural network. A novel multi-colorimetric technology is introduced to measure precisely the temporal evolutional characteristics of temperature and emissivity-area products. To test the effectiveness of the recognition algorithm, the results obtained from a set of synthetic multispectral data set are presented and discussed. These results indicate that the discrimination algorithm can obtain a remarkable success rate.

关 键 词：ALGORITHMS Behavioral research Feature extraction Statistical tests Time varying networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]