Subspace-based algorithms without eigendecomposition for array signal processing

A class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors is proposed. The methods estimate the DOA using only linear transformations of the data. This is of special interest for cases when the number of sensors is large and the computational advantages of these methods are significant. These methods use a less restrictive noise model and, e.g., can accommodate cases where the noise variance is different for different sensors. Large sample variance expressions for the estimates of the DOAs are derived, and the statistical properties of the proposed method are compared against the properties of multiple signal classification (MUSIC).<>