Integrated Snapshot and Filtering-based Bistatic Radio SLAM in mmWave Networks

Abstract

The inherent geometric relations between the propagation environment and millimeter-wave (mmWave) signals can be leveraged for simultaneous localization and mapping (SLAM) in 5G and beyond networks. Conventional solutions either rely on nonlinear filtering techniques or only solve the SLAM problem for a single user equipment (UE) location. This paper presents an integrated snapshot and filtering-based bistatic radio SLAM approach which utilizes low complexity linear Bayesian filtering equations for processing the measurements sequentially over time. The proposed method is validated using 60 GHz experimental data, indicating that the proposed processing pipeline combines good accuracy with low computational overhead, outperforming state-of-the-art benchmark algorithms.