  We investigate localization of a source based on angle of arrival (AoA)
measurements made at a geographically dispersed network of cooperating
receivers. The goal is to efficiently compute accurate estimates despite
outliers in the AoA measurements due to multipath reflections in
non-line-of-sight (NLOS) environments. Maximal likelihood (ML) location
estimation in such a setting requires exhaustive testing of estimates from all
possible subsets of "good" measurements, which has exponential complexity in
the number of measurements. We provide a randomized algorithm that approaches
ML performance with linear complexity in the number of measurements. The
building block for this algorithm is a low-complexity sequential algorithm for
updating the source location estimates under line-of-sight (LOS) environments.
Our Bayesian framework can exploit the ability to resolve multiple paths in
wideband systems to provide significant performance gains over narrowband
systems in NLOS environments, and easily extends to accommodate additional
information such as range measurements and prior information about location.
