Efficient adaptive reduced-rank multibeam processing

An implementation of the multistage Weiner filter (MWF) is developed for constrained filtering applications, such as radar surveillance, that require the formation of many filter vectors. The MWF is a "signal-dependent" reduced rank adaptive filter, which means that it uses the steering vector to form its basis for rank reduction. Signal-dependent processing provides a performance improvement over signal-independent methods, but typically incurs a computational burden that increases linearly with the number of filters. This paper describes a computationally efficient implementation of the MWF, based on the method of conjugate gradients (CG), and shows the relationship between MWF and CG. The CG-based technique uses a single SVD to impose a diagonal structure on the data matrix, and realizes an order-of-magnitude speed improvement over the conventional MWF.