Performance analysis for DOA estimation algorithms using physical parameters

Subspace-based direction-of-arrival (DOA) estimation has attracted many excellent performance studies, but limitations such as the analysis of individual algorithms generally exist in these performance studies. The authors previously proposed a unified performance analysis based on a finite amount of data, and achieved a tractable expression for the mean-squared DOA estimation error for the MUSIC, Min-Norm, ESPRIT, and state-space realization algorithms. However, this expression uses the singular values and vectors of a data matrix. Thus, the effects of the original data parameters such as numbers of sensors and snapshots, source coherence, and separations were not explicitly analyzed. In this work, the authors have further unified and simplified their previous results and derived a unified expression based on the original data parameters