基于智能体交互的无线电地图构建与非合作辐射源定位OA

Radio map construction and non-cooperative emitter localization are pivotal tasks for accurate perception and dynamic control of the electromagnetic environment,characterized by high coupling and mutual constraints.Drawing on cognitive learning and embodied intelligence theories,this paper proposes a unified dual-task framework for radio map construction and emitter localization featuring multi-agent collaborative interaction and endogenous intelligence,thereby es-tablishing a"perception-cognition-action"feedback loop.First,leveraging the concept of crowdsourced probabilistic con-sensus,we integrate Gaussian process regression(GPR)with 2D-positional encoding to transform discrete sparse sampling data from multiple agent into spatial attention maps.These maps encompass continuous signal field predictive distributions,geometric topologies,and probability estimates of emitter spatial presence,establishing a consensus perceptual prior regard-ing the target emitters.Building upon this,a consistent joint representation for emitters is constructed within a unified multi-agent semantic space.A multi-head self-attention mechanism is then utilized to design task-driven and target-driven agent interaction strategies,enabling the simultaneous execution of both tasks.Furthermore,a dual-task information interaction feedback strategy based on proximal policy optimization(PPO)is designed to achieve self-organization and self-learning,forming an embodied behavioral loop of perception,cognition,and interactive decision-making.Simulation results validate the feasibility and superiority of the proposed scheme.Under a 5% sparse sampling condition,the synergistic optimization of both tasks achieves accuracy improvements of 18.2% and 43.5% for radio map construction and emitter localization,re-spectively,compared to independent task execution.Specifically,the RMSE(Root Mean Square Error)of radio map con-struction improves by 60.95% and 32.55% over Kriging and full convolutional auto-encoder,respectively.Meanwhile,the localization error is reduced by 65.7% compared to the RSS-based baseline,maintaining a stable relative accuracy within 1.2% even when extended to a 10 km×10 km large-scale scenario.