  In this paper, we consider a novel and robust maximum likelihood approach to
localizing radiation sources with unknown statistics of the source signal
strength. The result utilizes the smallest number of sensors required
theoretically to localize the source. It is shown, that should the source lie
in the open convex hull of the sensors, precisely $N+1$ are required in
$\mathbb{R}^N, ~N \in \{1,\cdots,3\}$. It is further shown that the region of
interest, the open convex hull of the sensors, is entirely devoid of false
stationary points. An augmented gradient ascent algorithm with random
projections should an estimate escape the convex hull is presented.
