Advanced Matrix Factorization Techniques for Enhanced Signal Resolution in Array Processing

Accurate signal resolution is crucial in array processing, impacting numerous applications from radar systems to telecommunications. This paper introduces novel signal resolution methods leveraging advanced matrix factorization techniques. We begin by constructing a high-fidelity signal model using matrix factorization methods, which allows for an efficient representation of complex signals. Specifically, we employ techniques such as Non-negative Matrix Factorization (NMF) and Robust Principal Component Analysis (RPCA) to decompose and analyze signal matrices. Our approach contrasts traditional signal processing methods by offering higher accuracy in signal resolution while maintaining computational efficiency. The paper presents a comparative analysis with conventional techniques, showcasing the superiority of matrix factorization in complex signal scenarios.