Coherent Direct Multipath SLAM
Benjamin J. B. Deutschmann, Klaus Witrisal, Erik Leitinger

TL;DR
This paper introduces a scalable Bayesian direct MP-SLAM method for coherent data fusion in D-MIMO/XL-MIMO systems, enabling high-accuracy localization and environment mapping directly from raw RF signals.
Contribution
It proposes a novel phase-preserving likelihood function combined with a surface feature vector model for coherent, distributed RF signal processing in MIMO systems.
Findings
Performance surpasses existing noncoherent methods.
Approaches the posterior CRLB in simulations.
Supports near real-time processing with GPU acceleration.
Abstract
Challenging indoor and urban environments with severe multipath propagation and obstructed LoS (OLoS) degrade classical radio frequency (RF) positioning. Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising remedy, building and exploiting a map of the propagation environment to enhance the robustness. Emerging distributed multiple-input multiple-output (D-MIMO)/extremely large-scale MIMO (XL-MIMO) infrastructures, with single XL antenna arrays or distributed subarrays, offer large spatial apertures and enable high-resolution sensing, in particular when phase coherence is maintained across base stations (BSs), subarrays, or distributed arrays. In this work, we propose a scalable Bayesian direct MP-SLAM method for coherent data fusion in D-MIMO/XL-MIMO systems that jointly infers the environment while performing robust, high-accuracy localization directly from…
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