WDE-SBL: Waveform Design for SBL-Based Low- SNR Target Parameter Estimation in RFPA Radar

Abstract

Compressive sensing (CS)-based sparse Bayesian learning (SBL) algorithms for random frequency and pulse repetition interval agile (RFPA) radar exhibit poor robustness under low signal-to-noise ratio (SNR) conditions. To address this issue, this letter proposes a waveform-design-enhanced SBL (WDE-SBL) method. This method integrates waveform design into the SBL framework, which employs low-complexity complex Gaussian priors, without increasing the computational load. Specifically, for the first time, this letter derives the analytical expression of the grid energy associated with the SBL algorithm for RFPA radar, clearly revealing the contributions of individual components. Based on an in-depth analysis of this expression, the proposed WDE-SBL method constructs the objective function to suppress noise floor and designs the frequency-hopping sequence accordingly. Simulation results demonstrate that by incorporating waveform design, the proposed WDE-SBL can clearly identify targets and accurately estimate target parameters under low SNR conditions where other CS-based algorithms fail.