A target intention recognition method based on information classification processing and information fusion.

Intention recognition of non-cooperative target is an important basis for battlefield command decision-making. Recent advances suggest recognizing target intention from a perspective of data-driven. However, existing data-driven models do not consider complementary information between features to enhance their robustness in battlefield environments. To solve the problem, this paper constructs a novel neural network fusion model with information classification processing and information fusion to achieve target intention recognition. The model first designs the cross-classification processing method according to attributes' correlations and variation characteristics. Then, an interactive feature-level fusion method is proposed to model the fine-grained correlations between attributes to discover salient features. Finally, a decision-level fusion method based on Dempster–Shafer theory is proposed to fuse the complementary information among attributes. The experimental results show that the recognition accuracy of the proposed model can reach 89.63%, and it can be maintained above 75% under the conditions of severe attribute missing or noise interference. It is demonstrated that the proposed model has higher accuracy and robustness in battlefield incomplete information environments. • An artificial neural network model with information classification processing and information fusion is constructed for target intention recognition. • An interactive feature-level fusion method is proposed to model the fine-grained correlations between attributes to discover salient features. • A decision-level fusion method based on Dempster–Shafer theory is proposed to fuse the complementary information among attributes. • Experiments demonstrate that the proposed model has higher accuracy and robustness in battlefield incomplete information environments. [ABSTRACT FROM AUTHOR]