Research Of Situation Assessment And Threat Assessment Algorithms Based On Big Data Processing Technology

As the battlefields are complicated and temperamental in the modern warfare,huge chunks of data about battlefields collected by different kinds of sensors bring great difficulty to commanding work.The big data processing on the battleground is a hotspot worth studying,researching the situation assessment and the threat assessment of it is the object of this thesis.According to the weapon deployment between the enemy and the enemy,weapon characteristics of the enemy,as well as the environmental factors such as weather and terrain about the field,by analyzing the relationship about all the factors,we can get situation assessment forms reflecting the situation of current war and predict the future situation well-founded.Based on situation assessment,threat assessment can estimate the threat level to our units by the enemy formations or units and our units by the battlefield areas comprehensively,helping our military commanders to analyze the threat levels about enemy's units and battlefield areas,providing vital reference to them for transferring deployment quickly and combating threat targets accurately.On the basis of theory and technology of big data processing,this thesis make an intensive study of the situation and threat assessment,putting up with assessments based on big data processing to a greater degree,mainly including the following research contents:1.Study target-grouping based on fuzzy clustering and modularity density.Summarize their features and weaknesses.Come up with a possibilistic fuzzy c-means clustering algorithm based on modularity density combined with the advantages.Make the PFCM algorithm that originally need artificial setting number of clusters stay outstanding while the optimal number of clusters is automatically determined by different raw data sets.2.Aiming at target intention prediction of posture review,the research is based on dynamic bayesian network.Analyze the process and principle of dynamic bayesian network.Compare the structure of dynamic bayesian network with which of recurrent neural network.Prove recurrent neural network's scientificity and superiority while solving continuous time inference problems.3.Come up with a battlefield location threat assessment based on deep q-learning to provide new ideas and solutions to battlefield deployment.Besides,come up with an exponential decreased learning-rate algorithm to optimize the network training process,making deep q-learning armed with faster learning rate as well as better learning result,and show up the optimized effects of exponential decreased learning-rate algorithm by environmental modeling and simulating.4.Design and build a situation database,upload the real-time data managed by situation assessment and threat assessment to database according to raw sensor data,make great convenience for commanders to read and write interface efficiently and fast.