Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation

Abstract

In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE.