Data Fusion for Multipath-Based SLAM: Combining Information from Multiple Propagation Paths

Multipath-based simultaneous localization and mapping (SLAM) is an emerging paradigm for accurate indoor constrained by limited navigation resources. The goal of multipath-based SLAM to support the estimation time-varying positions mobile agents detecting localizing radio-reflective surfaces in environment. In existing Bayesian methods, a propagation surface represented mirror image each physical anchor (PA) across that – known as corresponding “virtual anchor” (VA). Due this Vas representation, path mapped individually. Existing methods thus neglect inherent geometrical constraints different paths interact with same surface, which limits accuracy speed. paper, we introduce improved statistical model method enables data fusion SLAM. By directly representing master virtual (MVA), are also modeled statistically. A key aspect proposed based on MVAs check availability single-bounce double-bounce at potential agent means raytracing (RT). This integrated into detection probabilities paths. Estimation performed sum-product algorithm (SPA) derived factor graph represents new model. Numerical results simulated real demonstrate significant improvements compared state-of-the-art methods.