In signal enhancement applications, a reference signal which provides information about interferences and noise is desired. It can be obtained via a multichannel filter that performs a spatial null in the target position, a so-called target-cancelation filter. The filter must adapt to the target position, which is difficult when noise is active. When the target location is confined to a small area, a solution could be based on preparing a bank of target-cancelation filters for potential positions of the target. In this paper, we propose two methods to learn such banks from noise-free recordings. We show by experiments that learned banks have practical advantages compared to banks that were prepared manually by collecting filters for selected positions.