An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis

RSS-based target localization algorithms are usually derived from channel path-loss model where the measurement noise is generally assumed to obey Gaussian distribution. In this paper, we approximate the realistic measurement noise distribution by a Gaussian mixture model and proposed an improved mixture noise analysis-based RSS target localization algorithm employing expectation maximization, called Gaussian mixture-expectation maximization (GMEM) approach, to estimate target coordinates iteratively, which can be efficiently used for tackling unknown parameters of maximum likelihood estimation and non-convex optimization. Simulations show a considerable performance gain of our proposed localization algorithm in 2-D wireless sensor network.