基于频率着色的稀疏贝叶斯宽带波达角估计方法

In this paper, the frequency coloring technique is extended to the sparse Bayesian learning (SBL) algorithm to improve its performance of weak target detection in an interference environment. By this SBL-FC method, the array-received data are transformed into different frequency bins through Fourier transformation, and the SBL is used to estimate the directions of arrivals in each frequency bin for obtaining the power spectrum. Unlike the conventional SBL that sums the results in all frequency bins, the frequency spectrum difference between the interference and the target is considered, and the result in each frequency bin is colored differently. Based on this, the tracks of the interference and the target are shown in the bearing and time record (BTR) with different colors, making the target easily to be detected. Simulation and experimental results confirm that the performance of the SBL algorithm for target detection is improved by considering the frequency spectrum difference between the interference and the target.