Multiscale Information-Driven Radar Resource Algorithm-Level Co-Scheduling for Joint Maneuvering Target Tracking and Anti-Noise Suppression Jamming

To enhance maneuvering target tracking and mitigate noise suppression interference under low radar interception probability, by exploiting the inter-task reusable information and coupling relationship, the proposed cognitive radar multidimensional resource management (CRMRM) strategy collaboratively optimizes multi-dimensional signal domains to improve the linked multi-task processing capability. Firstly, to characterize comprehensive multi-task performance and diversified constraints, the CRMRM-oriented task priority-guided hierarchical criterion function constructs the predictive conditional Bayesian risk lower bound (PC-BRLB) and frequency-domain anti-jamming reward, while treating the radar interception probability as a secondary constraint, alongside real-time spectrum limitation and resource constraint, to form multicomponent constraint term. By constructing the subchannel selection action to formalize frequency-domain resource, this reward integrates the trade-off between bandwidth utilization and interference avoidance, and future multistep reward impact based on state transition. Secondly, to tackle the underlying high-dimensional, multi-constraint problem, via criterion characteristics analysis, a multiscale information-driven algorithm-level collaborative iterative solution method is proposed to address discrete and continuous variables separately through strategy decomposition and problem segmentation. The TD Actor-Critic (TDAC) iteratively performs the temporal difference (TD) error-based policy and value update, addressing the delayed reward and future multistep effect in frequency-domain resource optimization. Leveraging two propositions, the radiated power and dwell time are then jointly analytically optimized under interception threshold, reducing variable dimension and computational complexity. The improved artificial bee colony (IABC) handles the PC-BRLB by balancing global exploration and local exploitation, converging to the optimal pulse length and frequency modulation (FM) rate. Simulation results confirm CRMRM's superiority over alternative methods.