ABSTRACT
Most high resolution direction-of-arrival (DOA) estimation methods rely on subspace or eigen-based information which can be obtained from the eigenvalue decomposition (EVD) of an estimated correlation matrix, or from the singular value decomposition (SVD) of the corresponding data matrix. However, the expense of directly computing these decompositions is usually prohibitive for real-time processing. Also, because the DOA angles are typically time-varying, repeated computation is necessary to track the angles. This has motivated researchers in recent years to develop low cost eigen and subspace tracking methods. Four basic strategies have been pursued to reduce computation: (1) computing only a few eigencomponents, (2) computing a subspace basis instead of individual eigencomponents, (3) approximating the eigencomponents or basis, and (4) recursively updating the eigencomponents or basis. The most efficient methods usually employ several of these strategies.
