This paper addresses target localization problems
in both non-cooperative and cooperative 3-D wireless sensor
network (WSN), for both cases of known and unknown sensor
transmit power, PT . We employ a hybrid system that fuses
distance and angle measurements, extracted from the received
signal strength (RSS) and angle-of-arrival (AoA) information,
respectively. Based on range and angle measurement models,
we derive a novel non-convex estimator based on the least
squares (LS) criterion. The derived non-convex estimator tightly
approximates the maximum likelihood (ML) one for small
noise. We then show that the developed estimator can be
transformed into a generalized trust region subproblem (GTRS)
framework, by following the squared range (SR) approach, for
non-cooperative WSN. For cooperative WSN, we show that
the estimator can be transformed into a convex problem by
applying appropriate semidefinite programming (SDP) relaxation
techniques. Moreover, we show that the generalization of the
proposed estimators for known PT is straightforward to the case
where PT is not known. Our simulation results show that the
new estimators have excellent performance, and are robust to not
knowing PT . The new estimators for non-cooperative localization
significantly outperform the existing ones, and our estimators
for cooperative localization show exceptional performance in all
considered settings.