This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). 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 measurement model and simple geometry, 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 levels. We show that the developed non-convex estimator is suitable for distributed implementation, and that it can be transformed into a convex one by applying a second-order cone programming (SOCP) relaxation technique. We also show that the developed non-convex estimator can be transformed into a generalized trust region sub-problem (GTRS) framework, by following the squared range (SR) approach. The proposed SOCP algorithm for known transmit powers is then generalized to the case where the transmit powers are different and not known. Furthermore, we provide a detailed analysis of the computational complexity of the proposed algorithms. Our simulation results show that the new estimators have excellent performance in terms of the estimation accuracy and convergence, and they confirm the effectiveness of combining two radio measurements.
- Angle-of-arrival (AoA)
- Generalized trust region sub-problem (GTRS)
- Received signal strength (RSS)
- Second-order cone programming (SOCP)
- Wireless localization
- Wireless sensor network (WSN)