diff --git a/v2/crates/wifi-densepose-calibration/src/lib.rs b/v2/crates/wifi-densepose-calibration/src/lib.rs
index 4e8f21aa..18a841ac 100644
--- a/v2/crates/wifi-densepose-calibration/src/lib.rs
+++ b/v2/crates/wifi-densepose-calibration/src/lib.rs
@@ -25,11 +25,13 @@ pub mod extract;
 pub mod specialist;
 pub mod bank;
 pub mod runtime;
+pub mod multistatic;
 
 pub use anchor::{Anchor, AnchorLabel, AnchorQuality, EnrollmentEvent, EnrollmentSession, Posture};
 pub use bank::SpecialistBank;
 pub use enrollment::{AnchorQualityGate, AnchorRecorder};
 pub use error::{CalibrationError, Result};
 pub use extract::AnchorFeature;
+pub use multistatic::MultiNodeMixture;
 pub use runtime::{MixtureOfSpecialists, RoomState};
 pub use specialist::{Specialist, SpecialistKind, SpecialistReading};
diff --git a/v2/crates/wifi-densepose-calibration/src/multistatic.rs b/v2/crates/wifi-densepose-calibration/src/multistatic.rs
new file mode 100644
index 00000000..4c52a55b
--- /dev/null
+++ b/v2/crates/wifi-densepose-calibration/src/multistatic.rs
@@ -0,0 +1,265 @@
+//! Multistatic fusion (ADR-029 / ADR-151) — combine several *co-located* nodes
+//! observing one room.
+//!
+//! More links = more geometric diversity, so a person hidden from one node's
+//! line of sight is caught by another. Each node carries its own room-calibrated
+//! [`SpecialistBank`] (its own baseline + anchors); this fuses their per-window
+//! readings into a single [`RoomState`]:
+//!
+//! - **presence** — OR across nodes (any node seeing a person wins);
+//! - **posture / breathing / heartbeat** — the highest-*confidence* node (best
+//!   viewpoint for that signal that window);
+//! - **restlessness** — max (any node detecting movement);
+//! - **anomaly / veto** — max / any (a single implausible node vetoes the room);
+//! - **stale** — any node's bank stale flags the fused result.
+//!
+//! This is *same-room* multistatic. Nodes in *different* rooms are a federation
+//! concern (ADR-105), not fusion — see ADR-151 §3.3.
+
+use std::collections::BTreeMap;
+
+use crate::bank::SpecialistBank;
+use crate::extract::Features;
+use crate::runtime::{MixtureOfSpecialists, RoomState};
+use crate::specialist::SpecialistReading;
+
+/// A bank plus the node's current baseline id (for per-node staleness).
+struct NodeEntry {
+    mixture: MixtureOfSpecialists,
+    baseline_id: String,
+}
+
+/// Fuses co-located nodes' specialist banks into one room state.
+#[derive(Default)]
+pub struct MultiNodeMixture {
+    nodes: BTreeMap<u8, NodeEntry>,
+}
+
+impl MultiNodeMixture {
+    /// Empty fusion set.
+    pub fn new() -> Self {
+        Self {
+            nodes: BTreeMap::new(),
+        }
+    }
+
+    /// Register a node's bank. `current_baseline_id` is the baseline the node is
+    /// observing now (drift vs the bank's training baseline → STALE).
+    pub fn add_node(&mut self, node_id: u8, bank: SpecialistBank, current_baseline_id: impl Into<String>) {
+        self.nodes.insert(
+            node_id,
+            NodeEntry {
+                mixture: MixtureOfSpecialists::new(bank),
+                baseline_id: current_baseline_id.into(),
+            },
+        );
+    }
+
+    /// Number of registered nodes.
+    pub fn node_count(&self) -> usize {
+        self.nodes.len()
+    }
+
+    /// Fuse per-node feature windows into one room state. Nodes without a feature
+    /// entry this window are skipped.
+    pub fn infer(&self, per_node: &BTreeMap<u8, Features>) -> RoomState {
+        let states: Vec<RoomState> = per_node
+            .iter()
+            .filter_map(|(id, f)| {
+                self.nodes
+                    .get(id)
+                    .map(|e| e.mixture.infer(f, &e.baseline_id))
+            })
+            .collect();
+
+        if states.is_empty() {
+            return RoomState::default();
+        }
+
+        let presence = fuse_presence(&states);
+        let anomaly = max_value(states.iter().map(|s| &s.anomaly));
+        // Conservative: a single node seeing a physically-implausible signal
+        // vetoes the room (anti-hallucination, same as the single-node runtime).
+        let vetoed = states.iter().any(|s| s.vetoed);
+        let present = presence.as_ref().map(|r| r.value > 0.5).unwrap_or(true);
+
+        // Vitals/posture only when present and not vetoed.
+        let (posture, breathing, heartbeat) = if present && !vetoed {
+            (
+                best_confidence(states.iter().map(|s| &s.posture)),
+                best_confidence(states.iter().map(|s| &s.breathing)),
+                best_confidence(states.iter().map(|s| &s.heartbeat)),
+            )
+        } else {
+            (None, None, None)
+        };
+
+        RoomState {
+            presence,
+            posture,
+            breathing,
+            heartbeat,
+            restlessness: max_value(states.iter().map(|s| &s.restlessness)),
+            anomaly,
+            vetoed,
+            stale: states.iter().any(|s| s.stale),
+        }
+    }
+}
+
+/// Presence: a person is present if ANY node sees one; confidence = max.
+fn fuse_presence(states: &[RoomState]) -> Option<SpecialistReading> {
+    let readings: Vec<&SpecialistReading> = states.iter().filter_map(|s| s.presence.as_ref()).collect();
+    if readings.is_empty() {
+        return None;
+    }
+    let any_present = readings.iter().any(|r| r.value > 0.5);
+    let confidence = readings
+        .iter()
+        .map(|r| r.confidence)
+        .fold(0.0f32, f32::max);
+    Some(SpecialistReading {
+        kind: readings[0].kind,
+        value: if any_present { 1.0 } else { 0.0 },
+        confidence,
+        label: Some(if any_present { "present" } else { "absent" }.into()),
+    })
+}
+
+/// Pick the highest-confidence reading across nodes.
+fn best_confidence<'a>(
+    readings: impl Iterator<Item = &'a Option<SpecialistReading>>,
+) -> Option<SpecialistReading> {
+    readings
+        .flatten()
+        .fold(None::<&SpecialistReading>, |best, r| match best {
+            Some(b) if b.confidence >= r.confidence => Some(b),
+            _ => Some(r),
+        })
+        .cloned()
+}
+
+/// Pick the reading with the maximum value across nodes (movement / anomaly).
+fn max_value<'a>(
+    readings: impl Iterator<Item = &'a Option<SpecialistReading>>,
+) -> Option<SpecialistReading> {
+    readings
+        .flatten()
+        .fold(None::<&SpecialistReading>, |best, r| match best {
+            Some(b) if b.value >= r.value => Some(b),
+            _ => Some(r),
+        })
+        .cloned()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::anchor::AnchorLabel;
+    use crate::extract::AnchorFeature;
+
+    fn af(label: AnchorLabel, variance: f32, motion: f32) -> AnchorFeature {
+        AnchorFeature {
+            room_id: "r".into(),
+            label,
+            features: Features {
+                mean: 1.0,
+                variance,
+                motion,
+                breathing_score: 0.0,
+                breathing_hz: 0.0,
+                heart_score: 0.0,
+                heart_hz: 0.0,
+            },
+        }
+    }
+
+    fn bank(baseline: &str) -> SpecialistBank {
+        let anchors = vec![
+            af(AnchorLabel::Empty, 1.0, 0.1),
+            af(AnchorLabel::StandStill, 10.0, 0.2),
+            af(AnchorLabel::Sit, 6.0, 0.2),
+            af(AnchorLabel::SmallMove, 4.0, 1.2),
+            af(AnchorLabel::SleepPosture, 3.0, 0.1),
+        ];
+        SpecialistBank::train("r", baseline, &anchors, 1).unwrap()
+    }
+
+    fn live(variance: f32, motion: f32, br_hz: f32, br_score: f32) -> Features {
+        Features {
+            mean: 1.0,
+            variance,
+            motion,
+            breathing_score: br_score,
+            breathing_hz: br_hz,
+            heart_score: 0.0,
+            heart_hz: 0.0,
+        }
+    }
+
+    #[test]
+    fn two_nodes_register() {
+        let mut m = MultiNodeMixture::new();
+        m.add_node(1, bank("b1"), "b1");
+        m.add_node(2, bank("b2"), "b2");
+        assert_eq!(m.node_count(), 2);
+    }
+
+    #[test]
+    fn presence_or_across_nodes() {
+        let mut m = MultiNodeMixture::new();
+        m.add_node(1, bank("b1"), "b1");
+        m.add_node(2, bank("b1"), "b1");
+        // Node 1 sees nobody (low variance), node 2 sees a person (high variance).
+        let mut per = BTreeMap::new();
+        per.insert(1u8, live(1.0, 0.1, 0.0, 0.0));
+        per.insert(2u8, live(12.0, 0.2, 0.3, 0.9));
+        let s = m.infer(&per);
+        assert_eq!(s.presence.unwrap().value, 1.0, "any node present → present");
+        assert!(s.breathing.is_some());
+    }
+
+    #[test]
+    fn breathing_picks_best_confidence_node() {
+        let mut m = MultiNodeMixture::new();
+        m.add_node(1, bank("b1"), "b1");
+        m.add_node(2, bank("b1"), "b1");
+        let mut per = BTreeMap::new();
+        // Both present; node 2 has the stronger breathing periodicity.
+        per.insert(1u8, live(12.0, 0.2, 0.2, 0.4));
+        per.insert(2u8, live(12.0, 0.2, 0.3, 0.95));
+        let s = m.infer(&per);
+        let br = s.breathing.unwrap();
+        assert!((br.value - 18.0).abs() < 0.3, "picked 0.3 Hz node");
+        assert!(br.confidence > 0.9);
+    }
+
+    #[test]
+    fn anomaly_in_one_node_vetoes_room() {
+        let mut m = MultiNodeMixture::new();
+        m.add_node(1, bank("b1"), "b1");
+        m.add_node(2, bank("b1"), "b1");
+        let mut per = BTreeMap::new();
+        per.insert(1u8, live(12.0, 0.2, 0.3, 0.9));
+        per.insert(2u8, live(9000.0, 500.0, 0.0, 0.0)); // wild outlier
+        let s = m.infer(&per);
+        assert!(s.vetoed);
+        assert!(s.breathing.is_none());
+    }
+
+    #[test]
+    fn stale_node_flags_room() {
+        let mut m = MultiNodeMixture::new();
+        m.add_node(1, bank("b1"), "b2"); // trained on b1, now observing b2 → stale
+        let mut per = BTreeMap::new();
+        per.insert(1u8, live(12.0, 0.2, 0.3, 0.9));
+        assert!(m.infer(&per).stale);
+    }
+
+    #[test]
+    fn empty_window_safe() {
+        let m = MultiNodeMixture::new();
+        let s = m.infer(&BTreeMap::new());
+        assert!(s.presence.is_none());
+    }
+}