Localization Based on Improved Sparse Bayesian Learning in mmWave MIMO Systems

In the existingmillimeter-wave(mmWave) wireless positioning systems, the method based onsparse Bayesian learning(SBL) uses the channel sparsity to estimate the parameters required for positioning, such asangle of arrival(AOA) and time delay, but most existing SBL solutions only consider the angle sparsity. In this paper, we consider the joint sparsity of the angle domain and time delay domain to propose an improved SBL algorithm by using a new two-dimensional adaptive grid refinement method in the SBL framework. This algorithm solves the grid mismatch problem caused by the fixed grid in the traditional SBL method, and reduces the algorithm complexity of theoff-grid SBL(OGSBL) algorithm. We also obtain theCramér-Rao bound(CRB) of AOA, time delay and position estimation based on the mmWave multipath signals to analyze the estimation errors. Simulation results show that the performance of the proposed algorithm is better than existing algorithms and can approach CRB.