  This paper considers the problem of localising a stationary signal source
using a team of mobile agents which only take binary measurements. Background
false detection rates and missed detection probabilities are incorporated into
the framework. A Bayesian estimation algorithm that discretises the search
environment is employed, and analytical convergence and consistency results for
this are derived. Fisher Information is then used as a metric for the design of
optimal agent geometries. Knowledge of the probability of detection as a
function of the source and agent locations is assumed in the analysis, with
special attention given to range-dependent functions. The behaviour of the
algorithm under inexact knowledge of the probability of detection is also
analysed. Finally, simulation results are presented to demonstrate the
effectiveness of the algorithm.
