Advanced Techniques in Adaptive Beamforming for Enhanced DOA Estimation

Direction of Arrival (DOA) estimation remains a pivotal aspect in the field of signal processing, finding applications across diverse domains. This paper introduces an innovative approach to DOA estimation by integrating adaptive beamforming techniques. We start by exploring the adaptive array theory, focusing on pattern synthesis and beamforming algorithms to enhance signal clarity and direction detection. The paper then delves into the application of Machine Learning (ML) algorithms for dynamic environment adaptation and improved accuracy in DOA estimation. Our approach contrasts the conventional methods by leveraging adaptive beamforming combined with ML, resulting in not only enhanced estimation accuracy but also in adaptability to varying signal conditions. The paper presents comparative analyses with traditional methods, demonstrating the potential of this integrated approach in complex signal environments.