Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework

Recognizing target intent is crucial for making decisions on the battlefield. However, the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques. Facing with the challenge, a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval (IR) model and a Hybrid Intention Recognition (HIR) model. The target data acquired by the sensors are modelled as Basic Probability Assignments (BPAs) based on evidence theory to create uncertain datasets. Then, the HIR model is utilized to recognize intent for a tested sample from uncertain datasets. Finally, the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample. Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes. The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.