MVP: An Enhanced Weighted Subspace Fitting Algorithm for Improved DOA Estimation

In this paper, we introduce an enhanced weighted subspace fitting algorithm called MVP, specifically designed for direction of arrival (DOA) estimation. The MVP algorithm incorporates novel techniques and strategies that significantly improve estimation accuracy and robustness against noise and interference. Compared to conventional WSF methods, MVP demonstrates superior performance in handling noisy environments and signal interference scenarios. We provide a comprehensive implementation guide for the MVP algorithm, including its mathematical foundation and computational workflow. The algorithm implementation typically involves covariance matrix computation, eigenvalue decomposition for signal subspace extraction, and optimization techniques for parameter estimation. Key MATLAB functions that may be utilized include 'eig' for eigenvalue decomposition and optimization tools for solving the subspace fitting problem. This article aims to provide readers with deeper insights into subspace fitting algorithms and DOA estimation techniques, while offering valuable references for researchers in related fields.