Four-Stage Reconstruction-Based Direction Finding for Sparse Bistatic MIMO Radar

Numerous studies on direction finding have focused on traditional passive sparse arrays with enhanced degrees-of-freedom (DoFs). However, many unexplored areas remain in the realm of active sparse multiple-input multiple-output (MIMO) radar, which holds the potential for further enhancing DoFs. In this study, we concentrate on direction, specifically two-dimensional (2D) direction-of-arrival and 2D direction-of-departure, finding for sparse bistatic MIMO radar. Unlike most existing studies that utilize only partial (cross-) covariance data associated with different subarrays, we propose a four-stage reconstruction-based approach that reconstructs the covariance data for the entire transceiver step by step while maintaining low computational complexity, as it requires only addition operations. This process includes a data rearrangement procedure utilizing our defined selection matrices. To evaluate the performance of our proposed approach, we also derive the Cramér-Rao bound for the multi-parameters involved in our sparse bistatic MIMO radar framework. Numerical simulations are conducted to validate the advantages of the proposed method, including increased identifiability and improved estimation performance, thanks to the enhanced DoFs.