Performance analysis of fast projections of the Hung-Turner type for adaptive beamforming

Eigenvector-based projection methods are currently popular in direction-of-arrival (DOA) estimation and adaptive nulling of interference. The most significant problem with these methods is their very high computational load, particularly when used for large arrays of many elements. In this paper, we present a statistical performance analysis of a fast projection method of the Hung-Turner type (HTP) when used for interference cancellation, which overcomes the critical computational intensity. It is shown that the resulting normalized signal-to-noise-plus-interference-ratio (SNIR) for the HTP is a special case of that from the sample matrix inversion method with diagonal loading (LSMI). An analytical expression for the probability density function of the normalized SNIR is derived, from which the independency of performance of the interference power can be seen. The optimum number of snapshots, the achievable rate of convergence and the resulting loss of SNIR for arbitrary interference environments can be predicted from this analysis. As the ideas of interference suppression and high-resolution angle-of-arrival estimation are closely related, the presented results can also be translated to the superresolution problem.