Hierarchical radar target localization

We present a novel m-ary tree hierarchical search strategy for stationary radar target localization in the presence of white Gaussian noise. This is done in the context of a discretized version of the problem of optimal beamforming, or radar transmit and receive pattern design. We assume that the target is equally likely to be in one of M discrete cells and that we have L observations at our disposal. We recursively group the search cells into m groups until the size of each group reduces to one cell, thus creating a m-ary search tree of depth log_m(M). We, then, allocate the available L observations among the tree levels in a manner that maximizes the probability of correctly locating the target. We compare the performance of the novel search strategy with that of previous techniques and demonstrate its superior performance