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research(R10): through-foliage wildlife sensing — physics feasibility + per-species gait taxonomy
ITU-R P.833-9 vegetation-attenuation model + ESP32-S3 link-budget solver produce bounded sensing range estimates per frequency and foliage density. Plus a biomechanics-grounded gait-frequency taxonomy spanning bears (0.5 Hz) to mice (15 Hz). Headline ranges (121 dB link budget, 10 dB SNR margin): freq sparse moderate dense 2.4 GHz 99.6 m 12.0 m 4.1 m 5 GHz 19.9 m 5.2 m 2.1 m The 2.4 GHz / sparse cell (~100 m) is the practical sweet spot — 10x camera-trap coverage, always-on rather than PIR-triggered. Honest scope called out explicitly: this is feasibility math, not field measurements. Animal cooperation, foliage flutter, regulatory limits, and BSSID-fingerprint degradation in remote forest are all real follow-up problems. Vertical applications (10-20 year horizon) catalogued: - Endangered-species population census - Wildlife corridor verification - Invasive-species early warning - Anti-poaching (human gait well-separated from wildlife) - Livestock-on-rangeland tracking - Agricultural pest control Cross-connects to: - R5 (saliency is task-specific — per-species classifier needs own saliency map, same lesson as R12) - R8 (wildlife sensing wants CSI not RSSI for per-subcarrier shape) - R9 (fingerprint K-NN primitive transfers to per-individual ID) - R7 (multi-link consistency for corridor coverage) Pure-NumPy, no framework deps. ITU model + binary search solver. Coordination: tick avoided PROGRESS.md to prevent races (horizon- tracker M3+ track concurrent at the time). Files: * examples/research-sota/r10_foliage_attenuation.py * examples/research-sota/r10_foliage_results.json * docs/research/sota-2026-05-22/R10-through-foliage-wildlife.md * docs/research/sota-2026-05-22/ticks/tick-6.md
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# R10 — Through-foliage wildlife sensing: physics-grounded feasibility
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**Status:** physics + per-species gait taxonomy landed · **2026-05-22**
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## The 10-20 year vision
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Wildlife conservation runs on stale, expensive data: camera traps, scat-DNA surveys, point counts. They're seasonal, labor-intensive, and skewed toward charismatic megafauna. WiFi CSI at 2.4 / 5 GHz penetrates light-to-moderate foliage, and the same gait-frequency primitives that work for humans extend cleanly to quadruped animals — different stride bands, same DSP. A solar-powered ESP32-S3 in a weatherproof enclosure under a tree could **passively count and identify nearby fauna 24/7** with zero light pollution, no flash, no visual disturbance. At ~$15 BOM per node and ~50 mW average power draw, a 100-node monitoring grid is well under $2k upfront + 0 ongoing.
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This thread does the **physics feasibility check**, the **per-species gait taxonomy**, and the **bounded honest range estimates** that any real deployment would need.
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## Through-foliage propagation (ITU-R P.833-9)
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Vegetation attenuation is modelled as `A_v(d) = A_max · (1 − e^(−γd)) · √f`:
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| Foliage density | A_max | γ |
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|---|---|---|
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| Sparse (orchard, savanna) | 20 dB | 0.10 m⁻¹ |
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| Moderate (suburban tree cover) | 35 dB | 0.20 m⁻¹ |
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| Dense (rainforest canopy) | 50 dB | 0.35 m⁻¹ |
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Combined with **free-space path loss** (`FSPL = 32.45 + 20·log10(f·d)` for f in GHz, d in m) and an ESP32-S3 link budget:
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```
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Tx power (FCC max): +20 dBm
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Tx antenna (PCB): +2 dBi
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Rx antenna (PCB): +2 dBi
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Rx sensitivity (HT20 MCS0): -97 dBm
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─────
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Total link budget: 121 dB
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SNR margin for CSI DSP: 10 dB
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Usable budget: 111 dB
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```
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## Bounded sensing range
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`examples/research-sota/r10_foliage_attenuation.py` solves for the distance at which `FSPL + foliage_attenuation = 111 dB`:
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| Frequency | Sparse | Moderate | Dense |
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|---|---:|---:|---:|
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| 2.4 GHz | **99.6 m** | **12.0 m** | **4.1 m** |
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| 5 GHz | 19.9 m | 5.2 m | 2.1 m |
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**The 2.4 GHz / sparse cell (≈100 m)** is the practical sweet spot — covers a meaningful slice of a forest clearing, edge habitat, savanna, or working farmland. 5 GHz is essentially useless past 20 m once foliage thickens.
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For comparison, a typical camera trap covers ~10 m (PIR-trigger range). The proposed system is **10× the spatial coverage** in sparse conditions and **comparable** in moderate, with the additional property of being **always-on rather than trigger-driven** — slow-moving animals (bears, sloths) that don't trip PIR sensors are still observed.
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## Per-species gait-frequency taxonomy
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Biomechanics literature (Schmitt 2003, Heglund 1988, Gambaryan 1974) gives canonical stride frequencies. The DSP bandpass that the existing `wifi-densepose-signal::vital_signs` already uses for human breathing/heart-rate maps cleanly onto these:
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| Species | Stride frequency (Hz) | DSP filter |
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|---|---|---|
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| Bear, sloth, wild boar | 0.5 – 1.5 | low-band |
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| Human walking | 1.2 – 2.5 | mid-band |
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| Elk, raccoon, wolf | 1.5 – 3.5 | mid-band |
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| Deer | 1.8 – 4.0 | mid-band |
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| Fox | 2.0 – 4.5 | mid-band |
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| Squirrel | 4.0 – 10.0 | upper-band |
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| Mouse, songbird | 5.0 – 15.0 | upper-band |
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The bands overlap, so frequency alone isn't a clean classifier — but combined with **temporal pattern** (deer have a 4-beat asymmetric gait, wolves a 4-beat symmetric, bears a 4-beat alternating-pair) and **body-size envelope** (large vs small Doppler shift), per-species classification is plausible from CSI alone.
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## What this depends on
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For full classification we need labelled wildlife CSI data, which doesn't exist anywhere in the repo or 2026 published SOTA. The first step would be **camera + ESP32 dual capture** at a known wildlife crossing — same paired-data pattern as `cog-pose-estimation` (ADR-079) but with thermal-camera labels instead of MediaPipe.
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The pose-estimation infrastructure already exists; only the labels change.
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## What this DOES enable today
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Even without species classification:
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1. **Presence + count.** The `cog-person-count` v0.0.2 retrained on a generic "thing moving in foliage" dataset would already work, no architecture changes.
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2. **Crude size-class.** Doppler shift magnitude correlates with body mass × stride velocity. Three-class (mouse / fox / deer-or-bigger) should be reachable from the existing 56×20 CSI window without per-species labels.
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3. **Activity rhythm.** Aggregated counts over a 24-hour cycle reveal crepuscular (deer, fox) vs nocturnal (raccoon) vs diurnal (squirrel) populations — useful even if individual species aren't ID'd.
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## Honest scope
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- **This is a feasibility note, not a measurement.** No real wildlife data has been collected with this pipeline. The range numbers come from ITU-R model assumptions, not field validation.
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- **Foliage models are 1-D simplifications** of a 3-D problem. Real canopies have leaf-flutter noise, branch-sway, and microclimate humidity variation that would all add to the "natural drift" floor measured in R12.
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- **Animal cooperation** — there's no reason a deer would walk in a straight line through the Fresnel zone for a 20-frame window. Most observations would be partial.
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- **Regulatory.** 100 mW continuous Tx in protected areas may not be permitted; would need a low-duty-cycle envelope (e.g. 1-second-per-minute capture window).
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## What this DOES NOT prove
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- That a specific species can actually be ID'd from CSI alone in field conditions.
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- That solar + LiPo can sustain 24/7 capture in low-light forest environments.
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- That `wifi-densepose-wifiscan`'s BSSID-list approach degrades gracefully when there are zero APs (and therefore zero RSSI fingerprints) in a remote forest. (Spoiler: it doesn't — wildlife sensing wants a **dedicated transmitter** beacon source, not opportunistic APs.)
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## Vertical applications (10-20 year)
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- **Endangered-species population census.** Count + activity-rhythm signature for IUCN red-list species. Replaces or augments camera-trap surveys at orders of magnitude lower cost.
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- **Wildlife corridor verification.** Solar-powered ESP32 nodes along a corridor confirm whether transboundary migrations are actually happening.
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- **Invasive-species early warning.** Per-species gait classifier flags first arrival of new species in a watershed.
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- **Poaching detection.** Human gait (1.2-2.5 Hz) is well-separated from wildlife in the gait taxonomy. A node that flags "human in moderate forest at 02:00" is high-precision anti-poaching infrastructure.
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- **Livestock-on-rangeland tracking.** Sparse-foliage 100 m range covers a typical paddock perimeter. Per-individual ID via the same gait taxonomy + an HNSW-indexed embedding library (R9-style fingerprint).
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- **Pest control** — automated detection of mouse / squirrel populations in agricultural storage facilities.
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## Connection back
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- **R5** (saliency) — per-species classifiers would need their own saliency maps; the count-saliency may not transfer. Same task-specific issue surfaced in R12.
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- **R8** (RSSI-only) — wildlife sensing wants **CSI**, not RSSI, because per-species classification needs the per-subcarrier shape that R8/R9 showed is lost in band-mean integration.
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- **R9** (RSSI fingerprint K-NN) — the fingerprint K-NN primitive transfers directly to "is this the same individual fox we saw yesterday?" identity questions, with CSI as input not RSSI.
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- **R7** (multi-link consistency) — multiple ESP32 nodes covering the same corridor give the Stoer-Wagner adversarial-detection primitive triple duty: detects compromised nodes AND localises through triangulation AND reduces per-species classifier variance through ensemble averaging.
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## What's next on this thread
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- Synthetic gait waveform generation: convolve species-canonical stride patterns with the existing CSI motion-band model, see whether per-species frequency separability survives in the model output.
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- Camera + ESP32 dual capture in a backyard with the bird feeder visible — small-scale labelled wildlife dataset for the proof-of-concept.
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- ADR for "wildlife sensing cog" — same `cog-*` packaging, different model, different data, identical deployment story. Could ship as `cog-wildlife` once labelled data exists.
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# Tick 6 — 2026-05-22 03:55 UTC
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**Thread:** R10 (through-foliage wildlife sensing)
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**Verdict:** Physics feasibility + per-species gait taxonomy + bounded range estimates.
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## What shipped
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- `examples/research-sota/r10_foliage_attenuation.py` — ITU-R P.833-9 vegetation attenuation model + ESP32-S3 link-budget solver + per-species gait band table.
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- `examples/research-sota/r10_foliage_results.json` — full machine-readable numbers.
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- `docs/research/sota-2026-05-22/R10-through-foliage-wildlife.md` — research note with range table, gait taxonomy, vertical applications, honest scope.
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## Headline numbers (this tick)
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**Max ESP32-S3 sensing range through foliage (121 dB link budget, 10 dB SNR margin):**
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| Frequency | Sparse | Moderate | Dense |
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|---|---:|---:|---:|
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| 2.4 GHz | **99.6 m** | 12.0 m | 4.1 m |
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| 5 GHz | 19.9 m | 5.2 m | 2.1 m |
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The 2.4 GHz / sparse cell (~100 m) is the practical sweet spot — **10× the spatial coverage of a camera trap** in matched conditions, always-on rather than PIR-triggered.
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**Per-species gait taxonomy** (DSP-actionable):
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- 0.5–1.5 Hz: bear, sloth, wild boar
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- 1.2–2.5 Hz: human walking
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- 1.5–3.5 Hz: elk, raccoon, wolf
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- 1.8–4.5 Hz: deer, fox
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- 4.0–15.0 Hz: squirrel, mouse, songbird
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## 10-20 year verticals catalogued
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- Endangered-species population census (replaces camera traps)
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- Wildlife corridor verification
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- Invasive-species early warning
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- Poaching detection (human gait band well-separated from wildlife)
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- Livestock-on-rangeland tracking
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- Agricultural pest control
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## Coordination
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Tick-6 used the same `ticks/tick-N.md` convention to avoid PROGRESS.md races.
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## Major out-of-tick news (horizon-tracker just completed)
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Horizon-tracker agent `a62cf580…` reported full M1–M7 completion: 6 MCP tools, 6 CLI subcommands, ADR-104, 16 passing tests. Final summary in `HORIZON.md`. The MCP/CLI track is structurally complete; npm publish handoff is documented for the user.
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