Frequency-agile Coherent Radar Target Sidelobe Suppression Based on Sparse Bayesian Learning

Frequency-agile radar performs well in ECM, which makes up for the shortcomings of traditional radar. It has many advantages and has been widely used. The carrier of frequency-agile radar has changed, which makes the traditional method very difficult to deal with. The detection of the target sidelobe generated by the traditional matched filtering (MF) algorithm affects the detection of weak targets. After analysis, The target is sparse in the detection region and the sidelobe of the target can be suppressed by accurately reconstructing the target. In this paper, a Sparse Bayesian learning (SBL) method is proposed to solve the target suppression sidelobe. SBL algorithm has better performance in reconstruction. The article first gives the echo model of frequency-agile radar, then the sparse reconstruction model of frequency-agile radar is given. Subsequently, the mathematical models of sparse bayesian learning and $l_{1}$-norm are given, and then the experimental results are given. Finally, the effectiveness of the algorithm is verified by experimental results and correlation analysis