Cooperative localization in wireless sensor networks using combined measurements

This paper addresses node localization problem in a cooperative 3-D wireless sensor network (WSN), for both cases of known and unknown node transmit power, P_T. We employ a hybrid system that combines distance and angle measurements, extracted from the received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. Based on RSS measurement model and simple geometry, we derive a novel non-convex estimator based on the least squares (LS) criterion, which tightly approximates the maximum likelihood (ML) estimator for small noise. It is shown that the developed estimator can be transformed into a convex one by applying appropriate semidefinite programming (SDP) relaxation technique. Moreover, we show that the generalization of the proposed estimator for known P_T is straightforward to the case where P_T is not known. Our simulation results show that the new estimator has excellent performance in a great variety of considered scenarios, and is robust to not knowing P_T.