Signal-dependent reduced-rank multibeam processing [radar SIGPRO]

A new 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.