Near-Field Radio Simultaneous Localization and Mapping

Abstract

This letter proposes a near-field radio simultaneous localization and mapping (SLAM) method that explicitly leverages spherical-wave propagation for high-precision localization and sensing in sixth-generation networks. The proposed method redesigns the birth process of a multiple-model probability hypothesis density filter for landmark-type assignment by exploiting additional distance information available from spherical wavefronts. In addition, posterior Cramér-Rao bounds are derived to quantify the resulting information gain for vehicle state estimation and mapping. Simulation results demonstrate that the method improves vehicle state estimation, mapping accuracy, and computational efficiency.