Sequential techniques in hierarchical radar target localization

In this paper we investigate the use of multi-hypothesis sequential detection techniques to enhance effort allocation in hierarchical 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 beam-forming, 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 an 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. The main contribution of this paper is the use of multi-hypothesis sequential detection along with on-line effort allocation to enhance the performance of a constrained off-line allocation strategy. This approach is shown to have superior performance compared with the previously proposed unconstrained off-line allocation strategy