Gradient algorithm for quantization levels in distributed detection systems

An iterative gradient algorithm is presented for determining the quantization levels in each of a number of independent sensors so arranged as to pick up a common signal field. The system is to satisfy the Neyman-Pearson criterion that the probability of detection be maximum for a preassigned false-alarm probability. In general a number of local maxima exist, and the proposed method enables efficient search for these by starting from a variety of initial trial values.<>