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Posterior Cramér-Rao bounds on localization and mapping errors in distributed MIMO SLAM

Deutschmann, Benjamin J.B. ; Li, Xuhong LU ; Meyer, Florian and Leitinger, Erik (2025)
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 Cramér-Rao lower bound on the position and orientation of specular surfaces, for RF-SLAM. In particular, we... (More)
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 Cramér-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. (Less)
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publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Asilomar Conference on Signals, Systems, and Computers 2025
pages
8 pages
language
English
LU publication?
yes
id
50903e63-d6e9-4283-86e4-41128c0eabc2
alternative location
https://arxiv.org/abs/2506.19957
date added to LUP
2026-01-04 23:40:20
date last changed
2026-01-08 10:06:23
@inproceedings{50903e63-d6e9-4283-86e4-41128c0eabc2,
  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 Cramér-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.}},
  author       = {{Deutschmann, Benjamin J.B. and Li, Xuhong and Meyer, Florian and Leitinger, Erik}},
  booktitle    = {{Asilomar Conference on Signals, Systems, and Computers 2025}},
  language     = {{eng}},
  title        = {{Posterior Cramér-Rao bounds on localization and mapping errors in distributed MIMO SLAM}},
  url          = {{https://arxiv.org/abs/2506.19957}},
  year         = {{2025}},
}