FBC-SBL: Frequency Band Clustering Sparse Bayesian Learning for Off-Grid Wideband DOA Estimation With Different Frequency Bands

Direction-of-arrival (DOA) estimation methods for wideband signals based on sparse Bayesian learning (SBL) have shown impressive performance. It generally assumes that wideband signals share the same frequency band, whereas deteriorating the performance of DOA estimation when target signals are located at different bands. To deal with this problem, we propose an off-grid DOA estimation framework, namely, frequency band clustering (FBC)-SBL, based on SBL incorporating FBC. FBC can exploit the cluster structure of signal distribution in frequency domain by applying a specific clustering algorithm. Compared with other existing algorithms, FBC-SBL can infer the frequency band occupation, thereby estimating DOAs more accurately. Specifically, the whole frequency band is first divided into finer subbands, and the variance vector of each subband is estimated. Then, FBC gathers the subbands with similar signal distributions. Finally, the estimated DOAs can be obtained by analyzing each cluster through SBL framework. Numerical simulations and real data experiments show that our method has superior performance compared with other algorithms and achieves an optimal average absolute error of 1.64° in real data experiments.