LAUSR

This letter addresses the problem of robust differential received signal strength (DRSS) based localization in the presence of generalized Gaussian noise. Instead of transforming the nonlinear equation into a pseudo-linear equation, we develop a maximum likelihood (ML) estimator which is based on the unconstrained $l_{p}$ -norm optimization problem for highly nonlinear cost functions. A non-iterative Monte Carlo importance sampling (MCIS) method is proposed to solve the optimization problem. To obtain the global optimal solution using relatively a fair number of random particles, a robust $l_{1}$ -norm based estimator for initial position is developed by solving the linear programming problem. The MCIS- $l_{1}$ method can yield a nearly unbiased estimate and avoid derivation or matrix multiplication operations compared to these algorithms that are based on pseudo-linear measurement equations. Simulation shows that the localization algorithm proposed in this letter achieves significant performance improvement in wireless sensor network localization with generalized Gaussian noise.