Abstract: To research the issue of how to grasp the commander's cognitive experience successfully and effectively facing to battlefields sight map, Convolution Neural Network (CNN) as a kind of the typical algorithm in deep learning can provide the key ways. However, CNN needs the enough samples for running. These samples are hardly to achieve for the time being. Aimed at these problems, some exploring researches were carried out. The issues of battlefields encompassing situation cognition met generally in the warfare and lacking enough samples were discussed. On the basis of analyzing the image characteristics of battlefields encompassing situation and the operational principles of CNN, a new method of battlefields encompassing situation cognition based on CNN without enough samples was proposed. In the method, the non-linear fitting function of CNN and the symmetry characteristics of the battlefields encompassing situation images were utilized to catch the commander's experience for cognizing the battlefields encompassing situation at a certain extent. Simulation results validate the effectiveness and the robustness of the proposed method.

Key words: battlefields encompassing situation cognition, commanders, method of establishing models, CNN, insufficient samples, deep learning