Adaptive Multipath-Based SLAM for Distributed MIMO Systems
- Published
- Tue, Jul 01, 2025
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- rotm
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Localizing users and mapping the environment using radio signals is a key task in emerging applications such as reliable, low-latency communications, location-aware security, and safety-critical navigation. Recently introduced multipath-based simultaneous localization and mapping (MP-SLAM) can jointly localize a mobile agent (i.e., the user) and the reflective surfaces (such as walls) in radio frequency (RF) environments with convex geometries. Most existing MP-SLAM methods assume that map features and their corresponding RF propagation paths are statistically independent. These existing methods neglect inherent dependencies that arise when a single reflective surface contributes to different propagation paths or when an agent communicates with more than one base station (BS).
In our paper [1], we propose a Bayesian MP-SLAM method for distributed MIMO systems that addresses this limitation. In particular, we make use of amplitude statistics to establish adaptive time-varying detection probabilities. Based on the resulting “soft” ray-tracing strategy, our method can fuse information across propagation paths in RF environments with nonconvex geometries. A Bayesian estimation method for the joint estimation of map features and agent position is established. Our method is validated using synthetic RF measurements in a challenging scenario with nonconvex geometry and capable of early detection of new surfaces solely through double-bounce paths. Our algorithm provides accurate localization and mapping estimates and attains the posterior Cramér-Rao lower bound (PCRLB) [2].
[1] If you are interested have a look here.
[2] If you are interested have a look here.
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