PERFORMANCE ANALYSIS FOR DOA ESTIMATION ALGORITHMS - UNIFICATION, SIMPLIFICATION, AND OBSERVATIONS

Subspace based direction-of-arrival (DOA) estimation has attracted many excellent performance studies, but limitations such as the assumption of an infinite amount of data and analysis of individual algorithms generally exist in these performance studies. We have 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 multiple signal classification (MUSIC), Min-Norm, estimation of signal parameters via rotational invariance techniques (ESPRIT), and State-Space Realization (SSR) algorithms. However, this expression uses the singular values and vectors of a data matrix which arc obtained by the highly nonlinear transformation of the singular value decompisition (SVD). Thus the effects of the original data parameters such as numbers of sensors and snapshots, source coherence and separations were not explicitly analyzed. Here we have made significant further unification and simplification of our previous result, and derived a unified expression based on the original data parameters. We then analytically observe the effects of these parameters on the estimation error. In addition, some interesting phenomena are discovered such as the fact that not all the algorithms have the property that additional sensors give better performance.