Method for Solving Signal Direction of Arrival (DOA) Estimation

In this article, we introduce a method for solving signal Direction of Arrival (DOA) estimation. DOA estimation is a critical task with applications in wireless communications, radar systems, and acoustic localization. This approach first transforms the DOA estimation problem into a Multiple Measurement Vector (MMV) problem, which involves matrix estimation. From an implementation perspective, this typically requires constructing a measurement matrix where each column represents a different sensor's received signal. Subsequently, we apply joint sparse constraints to reformulate it as a sparse-constrained optimization problem. Algorithmically, this can be implemented using techniques like group sparsity regularization or mixed-norm optimization (e.g., l2,1-norm minimization) to enforce the joint sparsity pattern across measurement vectors. This method not only enhances the accuracy of DOA estimation but also provides deeper insights into fundamental concepts in signal processing. The approach demonstrates significant potential for broad applications in signal processing, particularly in scenarios requiring high-resolution spatial spectrum estimation.