The hierarchical SVDDBNs based on modularization concept for air target recognition

Hao Fan, Xiaoguang Gao, Haiyang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The process of Air target identification is hierarchical, and is also a process of data fusion of diversified information obtained in the unstable time domain. In this paper, the process of air target recognition is regarded as a process of a qualitative iriference. According to the features that the hierarchy of air target identification process and the input parameters obtained in the unstable time domain, we constructed the air target recognition model on hierarchical structure-varied discrete dynamic bayesian networks (hierarchical SVDDBNs) by modularization concept. The air target identification model has such features, that is , the constructed model can real-time reconstructed the networks and finish the tasks flexibly by the features of the input data. In constructing bayesian networks model, the changes of structure is regular, in addition, the number of network nodes don't influence the decouple of the state of network nodes each other. Such can avoid structure learning and parameters learning. In the paper, the inference algorithm is presented, and simulation results show the feasibility of this approach.

Original languageEnglish
Title of host publication2010 2nd International Conference on Computational Intelligence and Natural Computing, CINC 2010
Pages33-38
Number of pages6
DOIs
StatePublished - 2010
Event2010 2nd International Conference on Computational Intelligence and Natural Computing, CINC 2010 - Wuhan, China
Duration: 13 Sep 201014 Sep 2010

Publication series

Name2010 2nd International Conference on Computational Intelligence and Natural Computing, CINC 2010
Volume2

Conference

Conference2010 2nd International Conference on Computational Intelligence and Natural Computing, CINC 2010
Country/TerritoryChina
CityWuhan
Period13/09/1014/09/10

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