AN IMPROVED COMPRESSIVE SENSING ALGORITHM BASED ON SPARSE BAYESIAN LEARNING FOR RFPA RADAR

The theory of Compressive Sensing (CS) has garnered significant attention in recent years due to its distinct advantages in mitigating the high sidelobe of Random Frequency and Pulse Repetition Interval Agile (RFPA) radar systems. Especially those CS algorithms based on Sparse Bayesian Learning (SBL), have played a pivotal role in enhancing signal recovery performance. Nevertheless, it is crucial to recognize that these algorithms possess certain limitations for RFPA radar, particularly in scenarios involving closely spaced targets, such as restricted dynamic range for target detection, slow convergence rates, and high computational complexity. To address these issues, this paper presents an improved SBL-based CS algorithm for RFPA radar systems. Specifically, the proposed algorithm enhances signal sparsity through the selection of a prior distribution, thereby improving its capability to detect between weak and strong targets. Additionally, the algorithm combines the Expectation-Maximization (EM) algorithm with a fixed-point update strategy, efficiently utilizing diagonal elements to expedite convergence while reducing computational complexity. Simulation results demonstrate that, in scenarios involving closely spaced targets, the proposed algorithm can effectively mitigate the masking of weak targets by strong target echoes, while exhibiting accelerated convergence with reduced computational overhead.