Rapidly adaptive nulling of interference

A simpler and more general analysis is presented of a previously reported principal component inverse (PCI) method of rapidly adaptive nulling of interference. It is assumed that the interference consists of a strong Gaussian component with a rank deficient covariance matrix plus a weak component of white Gaussian background noise. An approximate beta probability density function of the output signal-to-noise ratio (SNR) for the PCI method is derived. Using the theoretically derived formulas, it is shown that the PCI method requires much less data to produce a given, needed level of SNR with higher probability than the sample matrix inverse (SMI) method based on the inverse of the sample covariance matrix. The approximations and the final probability density function are tested through computer simulation. They accurately explain the experimental results.<>