DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.

Automated summary

An effective off-grid Sparse Bayesian Learning (SBL) method is proposed for target localization in FDA-MIMO radar, tackling the issues of high computational complexity and estimation performance degradation. By splitting angle-dependent components, optimizing DOA and range estimates, and iteratively updating grid points, the proposed method demonstrates effectiveness and superiority through theoretical analyses and numerical simulations.