Posterior Cram\'er-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM

TL;DR

This paper derives the theoretical lower bounds on localization and mapping errors in RF-SLAM, providing a benchmark for evaluating the performance of algorithms that jointly estimate positions and environmental maps in wireless networks.

Contribution

It introduces the mapping error bound (MEB) for RF-SLAM, extending performance analysis to map estimation, and demonstrates that existing algorithms approach this bound asymptotically.

Findings

The derived MEB applies to scenarios with single- and double-bounce reflections.

State-of-the-art RF-SLAM algorithms asymptotically reach the MEB.

The bounds evaluate both localization and mapping performance in RF-SLAM.

Abstract

Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well established, the literature lacks performance bounds for map information. This paper derives the mapping error bound (MEB), i.e., the posterior Cram\'er-Rao lower bound on the position and orientation of specular surfaces, for RF-SLAM. In particular, we consider a very general scenario with single- and double-bounce reflections, as well as distributed anchors. We demonstrate numerically that a state-of-the-art RF-SLAM algorithm asymptotically converges to this MEB. The bounds assess not only the localization (position and orientation) but also the mapping performance of RF-SLAM algorithms in terms of global features.