Soft-Coherent Direct Multipath SLAM

Abstract

Challenging indoor and urban environments with severe multipath propagation and obstructed line-of-sight degrade classical radio positioning. Multipath-based simultaneous localization and mapping (MP-SLAM) addresses this by building and exploiting propagation maps for robust localization. Emerging distributed multiple-input multiple-output (D-MIMO)/extremely large-scale MIMO (XL-MIMO) infrastructures provide large spatial apertures and high-resolution sensing, especially when phase coherence is maintained across base stations, subarrays, or distributed arrays. 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 raw radio signals. While commonly used zero-mean Type-II likelihood functions inherently lead to noncoherent processing across distributed arrays and thus to aperture loss, the proposed phase-preserving nonzero-mean Type-II likelihood shares a complex mean across distributed arrays. This enables coherent fusion and preserves the distributed aperture gain, while the variance captures noncoherent signal power. The method is combined with a surface model that enables map-feature fusion across the distributed infrastructure and supports near-field propagation and visibility effects. Bayesian inference is performed using belief propagation by means of the sum-product algorithm on a factor graph with particle-based messages. Parallelizing over particles and arrays, the GPU-accelerated implementation achieves millisecond-level runtimes even in large or distributed infrastructures. Simulation results show that the proposed method achieves performance gains over existing noncoherent methods and approaches the corresponding posterior CRLB, highlighting the potential of coherent processing for high-resolution sensing and localization.