A Novel Snapshot-Independent DoA Estimation Method for Smart Antennas

Abstract

Direction-of-Arrival (DoA) estimation is a significant component of smart antenna systems, enabling efficient beamforming, interference mitigation, and advanced spatial signal processing in modern wireless communications. Conventional high-resolution techniques, such as Multiple Signal Classification (MUSIC), require multiple signal snapshots to construct reliable covariance matrices, increasing computational complexity and limiting their effectiveness in dynamic or real-time scenarios. In this work, a novel snapshot-independent DoA estimation method based on a modified MUSIC framework is proposed. The proposed approach eliminates the dependency on multiple observations by exploiting inherent signal characteristics and spatial correlation properties, thereby enabling accurate direction estimation from limited data. Furthermore, a modified data reconstruction strategy is introduced to address robustness issues and improve performance under practical noise conditions. The effectiveness of the proposed method is validated through comprehensive simulations involving single and multiple source scenarios, including closely spaced signals. The results demonstrate that the proposed approach achieves estimation accuracy comparable to the conventional MUSIC algorithm while significantly reducing computational complexity and processing time. These characteristics make the proposed method highly suitable for real-time smart antenna systems and next-generation wireless communication applications.