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To enhance the resource scheduling performance of phased array radar, we propose a dynamic adaptive resource scheduling algorithm based on synthesis priorities and pulse interleaving. This approach addresses the challenges of low efficiency, high loss ratios, and significant subjectivity in task assignment within phased array radar systems. We introduce a task synthesis priority design method that considers the working mode priority, deadlines, and time shift ratios. By implementing this method, we can increase the flexibility of task scheduling and improve the efficiency of radar processing tasks. Additionally, our proposed pulse interleaving method effectively utilizes the waiting periods between receiving and transmitting pulses to process other beams, thereby enhancing resource utilization. Simulation results demonstrate that the proposed scheduling algorithm significantly reduces time deviation ratios and scheduling failure rates while improving scheduling yield and time utilization ratios.

We proposed a novel dense line spectrum super-resolution algorithm, the DMRA, that leverages dynamical multi-resolution of atoms technique to address the limitation of traditional compressed sensing methods when handling dense point-source signals. The algorithm utilizes a smooth $\tanh$ relaxation function to replace the $\ell_0$ norm, promoting sparsity and jointly estimating the frequency atoms and complex gains. To reduce computational complexity and improve frequency estimation accuracy, a two-stage strategy was further introduced to dynamically adjust the number of the optimized degrees of freedom. The strategy first increases candidate frequencies through local refinement, then applies a sparse selector to eliminate insignificant frequencies, thereby adaptively adjusting the degrees of freedom to improve estimation accuracy. Theoretical analysis were provided to validate the proposed method for multi-parameter estimations. Computational results demonstrated that this algorithm achieves good super-resolution performance in various practical scenarios and outperforms the state-of-the-art methods in terms of frequency estimation accuracy and computational efficiency.