Battlefield Reconnaissance Intelligence Processing Based on Rough Sets Theory

In order to raise the efficiency, automatization and intelligentization of battlefield reconnaissance intelligence processing, intelligence processing system is discussed and feature reduction algorithm based on Rough Sets theory is adopted to extract feature information in battlefield reconnaissance intelligence processing, so that the intelligence processing objects are optimized. The decision tables for each failure source are built and the analysis rules rooting in Rough Sets reduction are applied to carry through intelligent analysis for the system. The cases studied show that Rough Sets method can lighten the work burden in feature selection and afford advantage for autonomic learning and decision-making during intelligent analysis.