A Bayesian Approach to Communication-Driven SLAM Based on Diffuse Reflection Model

Communication-driven simultaneous localization and mapping (SLAM) can provide high-precision user positioning and environment sensing based on transmissions of communication signals. In this letter, we propose a Bayesian approach for communication-driven SLAM based on diffuse reflection model. We first extract the soft information of all channel parameters from received communication signals, then derive the messages of the state observations for users and environments, and finally develop a probabilistic data association and data fusion method for mapping issues. The proposed approach is applied to both planar and non-planar scattering environments, as well as single-user and multi-user scenarios. Simulation results demonstrate the high precision and robustness of the proposed approach.