A Model for Battlefield Situation Change Rate Prediction Based on Deep Learning

doi:10.16182/j.issn1004731x.joss.201803003

Abstract

Abstract: To measure and estimate the uncertainty of the battlefield situation is of great significance for the commanders to plan the reconnaissance mission and reduce the risk of decision-making. Based on Shannon's information theory, firstly, methods and a model on measurement of situation change rate are proposed. Secondly, a scene with two-dimensional grid elements maneuvering is established, based on deep learning, the prediction method for maneuvering trend is explored. It is proved that cross entropy is equivalent to situation change rate. Finally, with the increase of the objective uncertainty, situation change rate and the accuracy of the forecast is analyzed. It is deduced that there is an upper limit on the prediction accuracy based on the learning model, and the upper limit is inversely proportional to the situation change rate.

Key words: information theory, situation awareness, deep learning, shannon entropy

CLC Number:

TP391.9

Tao Jiuyang, Wu Lin, Wang Chi, Chu Junda, Liao Ying, Zhu Feng. A Model for Battlefield Situation Change Rate Prediction Based on Deep Learning[J]. Journal of System Simulation, 2018, 30(3): 785-792.

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