{"paper":{"title":"Soft-Coherent Direct Multipath SLAM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A shared complex mean in the likelihood function enables coherent fusion for direct multipath SLAM in distributed MIMO systems.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Benjamin J. B. Deutschmann, Erik Leitinger, Klaus Witrisal","submitted_at":"2026-04-21T17:48:45Z","abstract_excerpt":"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.\n  We propose a scalable Bayesian "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Simulation results demonstrate performance gains over existing noncoherent methods and approach the corresponding posterior CRLB (PCRLB), highlighting the potential of coherent distributed arrays for high-resolution sensing and localization.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Phase coherence is maintained across base stations or subarrays, enabling the shared complex mean in the likelihood function for coherent fusion.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A scalable coherent MP-SLAM method uses a phase-preserving nonzero-mean Type-II likelihood with shared complex mean across arrays plus an SFV model to achieve robust localization and mapping directly from raw signals, outperforming noncoherent baselines in simulations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A shared complex mean in the likelihood function enables coherent fusion for direct multipath SLAM in distributed MIMO systems.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c3246734fe2ae80262740ff02bf7704298971102cc83665ce510604c8293a777"},"source":{"id":"2604.19723","kind":"arxiv","version":3},"verdict":{"id":"3e5e521c-2628-4272-8db5-8fa7c7ded635","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T01:24:43.543361Z","strongest_claim":"Simulation results demonstrate performance gains over existing noncoherent methods and approach the corresponding posterior CRLB (PCRLB), highlighting the potential of coherent distributed arrays for high-resolution sensing and localization.","one_line_summary":"A scalable coherent MP-SLAM method uses a phase-preserving nonzero-mean Type-II likelihood with shared complex mean across arrays plus an SFV model to achieve robust localization and mapping directly from raw signals, outperforming noncoherent baselines in simulations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Phase coherence is maintained across base stations or subarrays, enabling the shared complex mean in the likelihood function for coherent fusion.","pith_extraction_headline":"A shared complex mean in the likelihood function enables coherent fusion for direct multipath SLAM in distributed MIMO systems."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.19723/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T15:43:12.759051Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-20T02:38:16.626199Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ba6e2c48ab40d9d0040c035edcebc646280f4d8fd2175bc07cb88798f4940bba"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}