Efficient Multipath TOA Estimation in Large-Scale Scenarios: A Stepwise-Optimized SBL Approach with Coprime Sampling

High-precision time delay estimation algorithms are widely used in many applications. However, high-precision algorithms are characterized by high computational complexity. Data processing of large-scale multipath signals becomes a bottleneck. In this paper, we propose a Coprime Generalized Cross Correlation Sparse Bayesian Learning (Coprime GCC-SBL) algorithm to solve this problem. The algorithm is based on stepwise optimization and co-prime sampling techniques to reduce the signal dimension and solve the spatial dimension. Then, the sparse Bayesian learning method is used to accurately estimate the time of arrival (TOA) parameters of the multipath signal. Simulation results show that the proposed algorithm has more significant advantages in processing large-scale signals compared to the general SBL algorithm.