Abstract: Timely and accurate estimation of target intention plays an important role in battlefield command decision-making. Considering the complex battlefield environment， the high randomness and ambiguity of test data， information and knowledge， this paper proposes an air target intention recognition method based on evidence-network causal effect analysis. A causal evidence network model that is compatible with information characteristics and intention diversity is constructed by the d-separation test. The model takes into account the conditional independent relationship between domain knowledge and data， and embeds cognitive uncertainty in the inference engine to achieve intention estimation of aerial targets in uncertain battlefield environment. In addition， based on the proposed causal evidence network model and intervention operator， causal effects are calculated and mined among target intentions and attributes. Simulation analysis shows that the proposed method can deal with both random and cognitive uncertainties， overcoming the defect of lacking causal analysis of traditional methods. With the proposed method， deep cognition of target intention and battlefield situation elements and “effect traceability” and “effect recognition” of intention recognition can be realized.

Key words: intention estimation, uncertain information, d-separation, causal evidence network, structural causal model, causal effect analysis