Sparse Uniform Circular Array DOA Estimation Using Weighted Subspace Fitting (WSF)

This article presents the implementation of Direction of Arrival (DOA) estimation for sparse uniform circular arrays using Weighted Subspace Fitting (WSF) technique. WSF is a subspace decomposition-based estimation method where we decompose the signal into multiple subspaces and utilize these subspaces to compute DOA estimates. In the optimization process, we employ the steepest descent method to iteratively find the optimal solution, ensuring accuracy and stability in DOA estimation results. To better understand and apply the WSF algorithm, we will explore its characteristics and advantages in detail. We will discuss the application of WSF algorithm in DOA estimation and present case studies demonstrating its implementation in other domains. Through code examples, we will illustrate key implementation aspects including: signal subspace decomposition using eigenvalue decomposition, weight matrix calculation based on noise subspace properties, and iterative parameter optimization using gradient-based methods. Finally, we will summarize WSF's applications in DOA estimation and discuss future development trends. We will examine the algorithm's potential in various application scenarios and explore methods for further optimization to meet broader requirements. The implementation typically involves MATLAB functions such as eig() for eigenvalue decomposition, array manifold matrix construction for circular arrays, and custom optimization loops for the steepest descent method. This article aims to provide valuable insights and guidance to help researchers better understand and implement WSF algorithms in practical applications.