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Situaiton Assessment Using Probabilistic Graphical Models

Author: WangYiNan
Tutor: MaBo
School: Beijing Institute of Technology
Course: Computer Vision
Keywords: Situation Assessment HSC algorithm Probabilistic graphical models Group tree propagation algorithm Adaptive Genetic Algorithm
CLC: E917
Type: Master's thesis
Year: 2011
Downloads: 80
Quote: 0
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Abstract


Battlefield situation estimated secondary fusion of multi - source data fusion system , it comes from a variety of status information extraction and analysis of a data integration platform battlefield given the trend of the current battlefield enemy , and the results passed to the next level fusion system threat assessment system . Trend estimated finished three main functions: event detection , target classification , and trend forecast . Battlefield situation assessment theory -depth study of the following major elements : the target classification to current battlefield combat platforms polymerization for the target group , level of battlefield information to provide troops for the military decision-making . The traditional target classification generally use hierarchical clustering and K- means algorithm . Hierarchical clustering drawback is due to the diversity of the battlefield , so that the selected threshold as a termination condition is very difficult, resulting compiled group error . Defects of the K- means algorithm can easily fall into the local minimization , leading to wrong cluster center . In response to these problems , the paper of HSC - based target classification algorithm . Through rigorous mathematical transformation , this method will nondifferentiable objective function into a differentiable function with a single extreme point , to strike a global minimum of the objective function value through optimization method to obtain correct knitting group results . In this paper, based on probabilistic graphical model , trend forecasts depth study . This method first probabilistic graphical models into group tree , and then use the group tree propagation algorithm for probabilistic graphical model inference . Which probabilistic graphical model to the transformation of the group tree elimination sequence for triangulation of probabilistic graphical model , the traditional method is to manually enter the elimination sequence , but the efficiency of this method is relatively low , and it is easy to arrive at local optimal elimination sequence , thus affecting the efficiency of reasoning by the group tree . To address this issue , this paper uses triangulation method based on adaptive genetic algorithm , this method has a strong global search capability , able to accurately find the optimal elimination sequence , thereby improving the efficiency of the trend forecast .

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