Performance analysis of spatial filtering of RF interference in radio astronomy

Radio astronomical observations are increasingly contaminated by man-made RF interference (RFI). If these signals are continuously present, then they cannot be removed by the usual techniques of detection and blanking. We have previously proposed a spatial filtering technique, where the impact of the interferer is projected out from the estimated covariance data. Assuming that the spatial signature of the interferer is time-varying, several such estimates can be combined to recover the missing dimensions. We give a detailed performance analysis of this algorithm. It is shown that the spatial filter introduces a small increase in variance of the estimates (because of the loss in information) and that the algorithm is unbiased in case the true spatial signatures of the interferers are known but that there may be a bias in case the signatures are estimated from the same data. Some of the bias may be removed, and moreover, the bias only affects the auto-correlations, whereas the astronomical information is mostly in the cross-correlations.