Exploiting adaptive beamforming for compressive measurements

Beamformers are spatial filters that focus energy in a particular direction while attempting to eliminate interference from other directions. This paper compares several adaptive approaches that seek to provide detection performance equivalent to classical techniques while using fewer beams, a form of measurement compression. Using an apriori distribution on the source locations together with an initial set of beams as a starting point, these algorithms adaptively form a sequence of beams based on posterior distributions of the source locations. Two methods are considered: one attempts to maximize the trace of the Fisher information and the other maximizes mutual information based on a Gaussian posterior approximation.