Adaptive Identification and Recovery of Jointly Sparse Vectors

In this paper we present a novel approach to the solution of a sequence of SMV problems with a joint support. This type of problem arises in a number of applications such as multiband signal reconstruction and source localization. The approach we present is adaptive in that it solves it as a sequence of weighted SMV problems rather than collecting the measurement vectors and solving an MMV problem. The weights are adaptively updated from one instance to the next. This approach avoids delays and large memory requirements (at the cost of increased computational load) with the added capability of tracking changes in joint signal supports.