ADR-031: Project RuView -- Sensing-First RF Mode for Multistatic Fidelity Enhancement

1. Context

1.1 The Single-Viewpoint Fidelity Ceiling

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).

1.2 Three Fidelity Levers

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.

1.3 Why "Sensing-First RF Mode"

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.

1.4 What Already Exists

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

2. Decision

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.

2.1 Core Architecture

+-----------------------------------------------------------------+
|                    RuView Multistatic Pipeline                    |
+-----------------------------------------------------------------+
|                                                                   |
|  +----------+  +----------+  +----------+  +----------+          |
|  | Node 1   |  | Node 2   |  | Node 3   |  | Node N   |          |
|  | ESP32-S3 |  | ESP32-S3 |  | ESP32-S3 |  | ESP32-S3 |          |
|  |          |  |          |  |          |  |          |          |
|  | CSI Rx   |  | CSI Rx   |  | CSI Rx   |  | CSI Rx   |          |
|  +----+-----+  +----+-----+  +----+-----+  +----+-----+          |
|       |              |              |              |               |
|       v              v              v              v               |
|  +--------------------------------------------------------+      |
|  |              Per-Viewpoint Signal Processing             |      |
|  |  Phase sanitize -> Hampel -> BVP -> Subcarrier select   |      |
|  |              (ADR-014, unchanged per viewpoint)          |      |
|  +----------------------------+---------------------------+      |
|                               |                                   |
|                               v                                   |
|  +--------------------------------------------------------+      |
|  |              Per-Viewpoint AETHER Embedding              |      |
|  |  CsiToPoseTransformer -> 128-d contrastive embedding    |      |
|  |              (ADR-024, one per viewpoint)                |      |
|  +----------------------------+---------------------------+      |
|                               |                                   |
|                [emb_1, emb_2, ..., emb_N]                         |
|                               |                                   |
|                               v                                   |
|  +--------------------------------------------------------+      |
|  |        * RuView Cross-Viewpoint Fusion *                |      |
|  |                                                          |      |
|  |  Q = W_q * X,  K = W_k * X,  V = W_v * X              |      |
|  |  A = softmax((QK^T + G_bias) / sqrt(d))                |      |
|  |  fused = A * V                                          |      |
|  |                                                          |      |
|  |  G_bias: geometric bias from viewpoint pair geometry    |      |
|  |  (ruvector-attention: ScaledDotProductAttention)         |      |
|  +----------------------------+---------------------------+      |
|                               |                                   |
|                        fused_embedding                            |
|                               |                                   |
|                               v                                   |
|  +--------------------------------------------------------+      |
|  |              DensePose Regression Head                   |      |
|  |  Keypoint head: [B,17,H,W]                             |      |
|  |  Part/UV head: [B,25,H,W] + [B,48,H,W]                |      |
|  +--------------------------------------------------------+      |
+-----------------------------------------------------------------+

2.2 TDM Sensing Protocol

• 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).

2.3 Geometric Bias Matrix

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

G_bias[i,j] = w_angle * cos(theta_ij) + w_dist * exp(-d_ij / d_ref)

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.

2.4 Coherence-Gated Environment Updates

/// Only update environment model when phase coherence exceeds threshold.
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
}

2.5 Two Implementation Paths

ESP32 path: commodity, achievable today, targets Silver tier (tracking + pose quality). Cognitum path: higher fidelity, targets Gold tier (tracking + pose + vitals).

3. DDD Design

3.1 New Bounded Context: ViewpointFusion

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 },
}

3.2 Extended Bounded Contexts

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.

4. Implementation Plan (File-Level)

4.1 Phase 1: ViewpointFusion Core (New Files)

4.2 Phase 2: Signal Processing Extension

4.3 Phase 3: Hardware Protocol Extension

4.4 Phase 4: Training and Metrics

5. Three-Metric Acceptance Test

5.1 Metric 1: Joint Error (PCK / OKS)

5.2 Metric 2: Multi-Person Separation

5.3 Metric 3: Vital Sign Sensitivity

5.4 Tiered Pass/Fail

6. Consequences

6.1 Positive

• 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.

6.2 Negative

• 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).

6.3 Interaction with Other ADRs

7. References

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).