| Field | Value |

|-------|-------|

| **Status** | Proposed |

| **Date** | 2026-03-02 |

| **Deciders** | ruv |

| **Codename** | **RuView** -- RuVector Viewpoint-Integrated Enhancement |

| **Relates to** | ADR-012 (ESP32 Mesh), ADR-014 (SOTA Signal), ADR-016 (RuVector Integration), ADR-017 (RuVector Signal+MAT), ADR-021 (Vital Signs), ADR-024 (AETHER Embeddings), ADR-027 (MERIDIAN Cross-Environment) |

Current WiFi DensePose operates with a single transmitter-receiver pair (or single node receiving). This creates three fundamental limitations:

- **Body self-occlusion**: Limbs behind the torso are invisible to a single viewpoint.

- **Depth ambiguity**: Motion along the RF propagation axis (toward/away from receiver) produces minimal phase change.

- **Multi-person confusion**: Two people at similar range but different angles create overlapping CSI signatures.

The ESP32 mesh (ADR-012) partially addresses this via feature-level fusion across 3-6 nodes, but feature-level fusion cannot learn optimal fusion weights -- it uses hand-crafted aggregation (max, mean, coherent sum).

1. **Bandwidth**: More bandwidth produces better multipath separability. Currently limited to 20 MHz (ESP32 HT20). Wider channels (80/160 MHz) are available on commodity 802.11ac/ax APs.

2. **Carrier frequency**: Higher frequency produces more phase sensitivity. 2.4 GHz sees macro-motion; 5 GHz sees micro-motion; 60 GHz sees vital signs.

3. **Viewpoints**: More viewpoints from different angles reduces geometric ambiguity. This is the lever RuView pulls.

RuView is NOT a new WiFi standard. It is a sensing-first protocol that rides on existing silicon, bands, and regulations. The key insight: instead of upgrading the RF hardware, upgrade the observability by coordinating multiple commodity receivers.

| Component | ADR | Current State |

|-----------|-----|---------------|

| ESP32 mesh with feature-level fusion | ADR-012 | Implemented (firmware + aggregator) |

| SOTA signal processing (Hampel, Fresnel, BVP, spectrogram) | ADR-014 | Implemented |

| RuVector training pipeline (5 crates) | ADR-016 | Complete |

| RuVector signal + MAT integration (7 points) | ADR-017 | Accepted |

| Vital sign detection pipeline | ADR-021 | Partially implemented |

| AETHER contrastive embeddings | ADR-024 | Proposed |

| MERIDIAN cross-environment generalization | ADR-027 | Proposed |

RuView fills the gap: **cross-viewpoint embedding fusion** using learned attention weights.

Introduce RuView as a cross-viewpoint embedding fusion layer that operates on top of AETHER per-viewpoint embeddings. RuView adds a new bounded context (ViewpointFusion) and extends three existing crates.

- Coordinator (aggregator) broadcasts sync beacon at start of each cycle.

- Each node transmits in assigned time slot; all others receive.

- 6 nodes x 1.4 ms/slot = 8.4 ms cycle -> ~119 Hz aggregate, ~20 Hz per bistatic pair.

- Clock drift handled at feature level (no cross-node phase alignment).

The geometric bias `G_bias` encodes the spatial relationship between viewpoint pairs:

where:

- `theta_ij` = angle between viewpoint i and viewpoint j (from room center)

- `d_ij` = baseline distance between node i and node j

- `w_angle`, `w_dist` = learnable weights

- `d_ref` = reference distance (room diagonal / 2)

This allows the attention mechanism to learn that widely-separated, orthogonal viewpoints are more complementary than clustered ones.

pub fn coherence_gate(

phase_diffs: &[f32], // delta-phi over T recent frames

threshold: f32, // typically 0.7

) -> bool {

// Complex mean of unit phasors

let (sum_cos, sum_sin) = phase_diffs.iter()

.fold((0.0f32, 0.0f32), |(c, s), &dp| {

(c + dp.cos(), s + dp.sin())

});

let n = phase_diffs.len() as f32;

let coherence = ((sum_cos / n).powi(2) + (sum_sin / n).powi(2)).sqrt();

coherence > threshold

}

| Path | Hardware | Bandwidth | Per-Viewpoint Rate | Target Tier |

|------|----------|-----------|-------------------|-------------|

| **ESP32 Multistatic** | 6x ESP32-S3 ($84) | 20 MHz (HT20) | 20 Hz | Silver |

| **Cognitum + RF** | Cognitum v1 + LimeSDR | 20-160 MHz | 20-100 Hz | Gold |

ESP32 path: commodity, achievable today, targets Silver tier (tracking + pose quality).

Cognitum path: higher fidelity, targets Gold tier (tracking + pose + vitals).

**Aggregate Root: `MultistaticArray`**

pub struct MultistaticArray {

/// Unique array deployment ID

id: ArrayId,

/// Viewpoint geometry (node positions, orientations)

geometry: ArrayGeometry,

/// TDM schedule (slot assignments, cycle period)

schedule: TdmSchedule,

/// Active viewpoint embeddings (latest per node)

viewpoints: Vec<ViewpointEmbedding>,

/// Fused output embedding

fused: Option<FusedEmbedding>,

/// Coherence gate state

coherence_state: CoherenceState,

}

**Entity: `ViewpointEmbedding`**

pub struct ViewpointEmbedding {

/// Source node ID

node_id: NodeId,

/// AETHER embedding vector (128-d)

embedding: Vec<f32>,

/// Geometric metadata

azimuth: f32, // radians from array center

elevation: f32, // radians

baseline: f32, // meters from centroid

/// Capture timestamp

timestamp: Instant,

/// Signal quality

snr_db: f32,

}

**Value Object: `GeometricDiversityIndex`**

pub struct GeometricDiversityIndex {

/// GDI = (1/N) sum min_{j!=i} |theta_i - theta_j|

value: f32,

/// Effective independent viewpoints (after correlation discount)

n_effective: f32,

/// Worst viewpoint pair (most redundant)

worst_pair: (NodeId, NodeId),

}

**Domain Events:**

pub enum ViewpointFusionEvent {

ViewpointCaptured { node_id: NodeId, timestamp: Instant, snr_db: f32 },

TdmCycleCompleted { cycle_id: u64, viewpoints_received: usize },

FusionCompleted { fused_embedding: Vec<f32>, gdi: f32 },

CoherenceGateTriggered { coherence: f32, accepted: bool },

GeometryUpdated { new_gdi: f32, n_effective: f32 },

}

**Signal (wifi-densepose-signal):**

- New service: `CrossViewpointSubcarrierSelection`

- Consensus sensitive subcarrier set across all viewpoints via ruvector-mincut.

- Input: per-viewpoint sensitivity scores. Output: globally-sensitive + locally-sensitive partition.

**Hardware (wifi-densepose-hardware):**

- New protocol: `TdmSensingProtocol`

- Coordinator logic: beacon generation, slot scheduling, clock drift compensation.

- Event: `TdmSlotCompleted { node_id, slot_index, capture_quality }`

**Training (wifi-densepose-train):**

- New module: `ruview_metrics.rs`

- Three-metric acceptance test: PCK/OKS (joint error), MOTA (multi-person separation), vital sign accuracy.

- Tiered pass/fail: Bronze/Silver/Gold.

| File | Purpose | RuVector Crate |

|------|---------|---------------|

| `crates/wifi-densepose-ruvector/src/viewpoint/mod.rs` | Module root, re-exports | -- |

| `crates/wifi-densepose-ruvector/src/viewpoint/attention.rs` | Cross-viewpoint scaled dot-product attention with geometric bias | ruvector-attention |

| `crates/wifi-densepose-ruvector/src/viewpoint/geometry.rs` | GeometricDiversityIndex, Cramer-Rao bound estimation | ruvector-solver |

| `crates/wifi-densepose-ruvector/src/viewpoint/coherence.rs` | Coherence gating for environment stability | -- (pure math) |

| `crates/wifi-densepose-ruvector/src/viewpoint/fusion.rs` | MultistaticArray aggregate, orchestrates fusion pipeline | ruvector-attention + ruvector-attn-mincut |

| File | Purpose | RuVector Crate |

|------|---------|---------------|

| `crates/wifi-densepose-signal/src/cross_viewpoint.rs` | Cross-viewpoint subcarrier consensus via min-cut | ruvector-mincut |

| File | Purpose | RuVector Crate |

|------|---------|---------------|

| `crates/wifi-densepose-hardware/src/esp32/tdm.rs` | TDM sensing protocol coordinator | -- (protocol logic) |

| File | Purpose | RuVector Crate |

|------|---------|---------------|

| `crates/wifi-densepose-train/src/ruview_metrics.rs` | Three-metric acceptance test (PCK/OKS, MOTA, vital sign accuracy) | ruvector-mincut (person matching) |

| Criterion | Threshold |

|-----------|-----------|

| [email protected] (all 17 keypoints) | >= 0.70 |

| [email protected] (torso: shoulders + hips) | >= 0.80 |

| Mean OKS | >= 0.50 |

| Torso jitter RMS (10s window) | < 3 cm |

| Per-keypoint max error (95th percentile) | < 15 cm |

| Criterion | Threshold |

|-----------|-----------|

| Subjects | 2 |

| Capture rate | 20 Hz |

| Track duration | 10 minutes |

| Identity swaps (MOTA ID-switch) | 0 |

| Track fragmentation ratio | < 0.05 |

| False track creation | 0/min |

| Criterion | Threshold |

|-----------|-----------|

| Breathing detection (6-30 BPM) | +/- 2 BPM |

| Breathing band SNR (0.1-0.5 Hz) | >= 6 dB |

| Heartbeat detection (40-120 BPM) | +/- 5 BPM (aspirational) |

| Heartbeat band SNR (0.8-2.0 Hz) | >= 3 dB (aspirational) |

| Micro-motion resolution | 1 mm at 3m |

| Tier | Requirements | Deployment Gate |

|------|-------------|-----------------|

| Bronze | Metric 2 | Prototype demo |

| Silver | Metrics 1 + 2 | Production candidate |

| Gold | All three | Full deployment |

- **Fundamental geometric improvement**: Viewpoint diversity reduces body self-occlusion and depth ambiguity -- these are physics, not model, limitations.

- **Uses existing silicon**: ESP32-S3, commodity WiFi, no custom RF hardware required for Silver tier.

- **Learned fusion weights**: Embedding-level fusion (Tier 3) outperforms hand-crafted feature-level fusion (Tier 2).

- **Composes with existing ADRs**: AETHER (per-viewpoint), MERIDIAN (cross-environment), and RuView (cross-viewpoint) are orthogonal -- they compose freely.

- **IEEE 802.11bf aligned**: TDM protocol maps to 802.11bf sensing sessions, enabling future migration to standard-compliant APs.

- **Commodity price point**: $84 for 6-node Silver-tier deployment.

- **TDM rate reduction**: N viewpoints leads to per-viewpoint rate divided by N. With 6 nodes at 120 Hz aggregate, each viewpoint sees 20 Hz.

- **More complex aggregator**: Embedding fusion + geometric bias learning adds ~25K parameters on top of per-viewpoint AETHER model.

- **Placement planning required**: Geometric Diversity Index optimization requires intentional node placement (not random scatter).

- **Clock drift limits TDM precision**: ESP32 crystal drift (20-50 ppm) limits slot precision to ~1 ms, which is sufficient for feature-level fusion but not signal-level coherent combining.

- **Training data**: Cross-viewpoint training requires multi-receiver CSI captures, which are not available in existing public datasets (MM-Fi, Wi-Pose).

| ADR | Interaction |

|-----|------------|

| ADR-012 (ESP32 Mesh) | RuView extends the aggregator from feature-level to embedding-level fusion; TDM protocol replaces simple UDP collection |

| ADR-014 (SOTA Signal) | Per-viewpoint signal processing is unchanged; cross-viewpoint subcarrier consensus is new |

| ADR-016/017 (RuVector) | All 5 ruvector crates get new cross-viewpoint operations (see Section 4) |

| ADR-021 (Vital Signs) | Multi-viewpoint SNR improvement directly benefits vital sign extraction (Gold tier target) |

| ADR-024 (AETHER) | Per-viewpoint AETHER embeddings are the input to RuView fusion; AETHER is required |

| ADR-027 (MERIDIAN) | Cross-environment (MERIDIAN) and cross-viewpoint (RuView) are orthogonal; MERIDIAN handles room transfer, RuView handles within-room geometry |

1. IEEE 802.11bf (2024). "WLAN Sensing." IEEE Standards Association.

2. Kotaru, M. et al. (2015). "SpotFi: Decimeter Level Localization Using WiFi." SIGCOMM 2015.

3. Zeng, Y. et al. (2019). "FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas." MobiCom 2019.

4. Zheng, Y. et al. (2019). "Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi." (Widar 3.0) MobiSys 2019.

5. Yan, K. et al. (2024). "Person-in-WiFi 3D: End-to-End Multi-Person 3D Pose Estimation with Wi-Fi." CVPR 2024.

6. Zhou, Y. et al. (2024). "AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi." IEEE IoT Journal. arXiv:2309.16964.

7. Zhou, R. et al. (2025). "DGSense: A Domain Generalization Framework for Wireless Sensing." arXiv:2502.08155.

8. Chen, X. & Yang, J. (2025). "X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing." ICLR 2025. arXiv:2410.10167.

9. AM-FM (2026). "AM-FM: A Foundation Model for Ambient Intelligence Through WiFi." arXiv:2602.11200.

10. Chen, L. et al. (2026). "PerceptAlign: Breaking Coordinate Overfitting." arXiv:2601.12252.

11. Li, J. & Stoica, P. (2007). "MIMO Radar with Colocated Antennas." IEEE Signal Processing Magazine, 24(5):106-114.

12. ADR-012 through ADR-027 (internal).