Adds §2.0 — the primary MERIDIAN path is now a three-stage pipeline:
1. pre-train a CIG-MAE-style dual-stream (amplitude+phase) masked autoencoder
on heterogeneous CSI (data breadth > pose-net capacity — arXiv:2511.18792);
2. fine-tune the existing §2.1–§2.6 heads (17-kpt/DensePose, AETHER, domain-
adversarial, geometry-conditioned) on top of the pre-trained encoder;
3. adapt per-room with source-free unsupervised domain adaptation behind
coherence_gate.rs::Recalibrate (separate ADR).
§2.1+ is retained but re-framed as the fine-tune-stage head, not a from-scratch
design. Adds the supporting references (2511.18792, 2512.04723, 2605.01369,
2506.12052, ACM TOSN 10.1145/3715130) and points at the 2026-Q2 SOTA survey.
Co-Authored-By: claude-flow <ruv@ruv.net>
Deep SOTA research into WiFi sensing domain gap problem (2024-2026).
Proposes 7-phase implementation: hardware normalization, domain-adversarial
training with gradient reversal, geometry-conditioned FiLM inference,
virtual environment augmentation, few-shot rapid adaptation, and
cross-domain evaluation protocol.
Cites 10 papers: PerceptAlign, AdaPose, Person-in-WiFi 3D (CVPR 2024),
DGSense, CAPC, X-Fi (ICLR 2025), AM-FM, LatentCSI, Ganin GRL, FiLM.
Addresses the single biggest deployment blocker: models trained in one
room lose 40-70% accuracy in another room. MERIDIAN adds ~12K params
(67K total, still fits ESP32) for cross-layout + cross-hardware
generalization with zero-shot and few-shot adaptation paths.
Co-Authored-By: claude-flow <ruv@ruv.net>
ruv