Multi-Target Behavior and Intent Prediction Under Incomplete Perception

Predicting target intent in complex, dynamic multi-agent environments remains a formidable challenge due to incomplete perception and the highly dynamic nature of multi-target interactions. Conventional approaches—such as D-S evidence theory, expert systems, and Recurrent Neural Networks (RNNs)—are often constrained by data incompleteness and rigid behavioral assumptions, limiting their adaptability to dynamic high-value target identification and multi-target situational awareness on the ground. To address these challenges, a novel framework termed Threat Field–Gated Recurrent Unit (TF-GRU) is proposed. The TF-GRU framework integrates threat field modeling with a dynamic repair mechanism to enhance intent prediction under partial perception. Specifically, threat field modeling associates target attributes with intentions through the construction of static and dynamic threat fields, effectively capturing the temporal and semantic relationships among multiple targets. A particle filtering and dynamic time warping fusion strategy (PF-DTW) is employed to repair data gaps via short-term filtering and long-term trajectory matching, further refined by a neighborhood-angle constraint for accurate multi-target state estimation. In addition, trajectory and threat field features are processed using a Mish activation function and a threat-adaptive gating mechanism, which dynamically regulate information flow within the recurrent unit to model behavioral evolution. Experimental evaluations demonstrate that TF-GRU significantly enhances intent prediction accuracy under incomplete data conditions, thereby improving comprehensive situational awareness and supporting high-confidence decision-making in dynamic multi-target scenarios.