  Sensor selection is a useful method to help reduce data throughput, as well
as computational, power, and hardware requirements, while still maintaining
acceptable performance. Although minimizing the Cram\'er-Rao bound has been
adopted previously for sparse sensing, it did not consider multiple targets and
unknown source models. We propose to tackle the sensor selection problem for
angle of arrival estimation using the worst-case Cram\'er-Rao bound of two
uncorrelated sources. We cast the problem as a convex semi-definite program and
retrieve the binary selection by randomized rounding. Through numerical
examples related to a linear array, we illustrate the proposed method and show
that it leads to the selection of elements at the edges plus the center of the
linear array.
