Super-Resolution-Based Bayesian Learning for the Localization of Extended Targets in mmWave MIMO OFDM Systems

With the growing demand for integrated sensing and communication (ISAC) in next generation wireless networks, efficient target localization techniques conceived for mmWave MIMO systems have become increasingly important. In this context, we propose a Sparse Bayesian Learning (SBL)-aided extended target localization framework for orthogonal frequency division multiplexing (OFDM)-based mmWave MIMO systems. The proposed approach explicitly considers the impact of intercarrier interference (ICI) arising in mobile environments, which is often overlooked in conventional schemes. Our framework is designed for hybrid mmWave MIMO architectures, where the number of radio frequency (RF) chains is considerably lower than the number of antennas, ensuring hardware efficiency. To achieve high-precision target localization, we introduce a delay, Doppler, and angular (DDA)-domain representation of the target, enabling accurate estimation of target parameters. The proposed algorithm leverages the inherent three-dimensional (3D) sparsity in the DDA domain of the scattering environment and employs the powerful SBL framework for effective parameter estimation. Furthermore, to address practical scenarios where the actual target parameters may not align with finite-resolution grids, we develop an enhanced off-grid SBL (OSBL) method based on super-resolution principles. This recursive grid refinement approach progressively improves the estimation accuracy. Additionally, we derive the Cram´ er Rao bound (CRB) and Bayesian CRB to theoretically characterize the achievable estimation performance. Simulation results confirm that the proposed method significantly outperforms existing algorithms in terms of estimation accuracy and robustness.