Fast RSSD multi-target localization in NLOS environments

Abstract

Signal strength–based localization is commonly employed in wireless sensor networks due to its low complexity and simplicity. However, in non-line-of-sight (NLOS) environments with unknown transmit power, effective and efficient multi-target localization is a challenging task. In this paper, a fast multi-target localization based on a neural network (FMLNN) is proposed. The received signal strength difference (RSSD) is employed and NLOS bias is considered. Determining the maximum likelihood (ML) estimator is a complex and highly non-convex problem, so it is solved indirectly using a neural network. First, prior data composed of known target information and RSSD values are used in offline training to learn the nonlinear relationship. Then, the locations of multiple targets are estimated online using the trained network. Results are presented which show the proposed method provides fast and efficient localization of multiple targets, and has greater robustness to NLOS bias than conventional state-of-the-art methods.