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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#!/usr/bin/env python3
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"""R10 — through-foliage WiFi attenuation curves (ITU-R P.833 + per-species gait).
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See docs/research/sota-2026-05-22/R10-through-foliage-wildlife.md.
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Plots the ITU-R P.833 vegetation specific attenuation A_v over distance
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for 2.4 GHz and 5 GHz CSI bands across three foliage densities. Compares
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to a 1×1 SISO ESP32-S3's link budget to derive a maximum sensing range.
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Pure NumPy, no plotting libs — emits a JSON file with the curves so a
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downstream consumer can render them.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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def itu_p833_attenuation(freq_ghz: float, distance_m: float, foliage_density: str) -> float:
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"""ITU-R P.833 specific-attenuation model for in-foliage propagation.
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Simplified parameterisation:
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A_max = max attenuation through dense canopy (dB)
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gamma = decay coefficient (1/m)
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A_v(d) = A_max * (1 - exp(-gamma * d))
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Realistic A_max / gamma per density (calibrated against in-leaf summer
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deciduous from ITU-R P.833-9 Table 1 + simulation studies):
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sparse (orchard, savanna) A_max=20 dB, gamma=0.10
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moderate (suburban tree cover) A_max=35 dB, gamma=0.20
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dense (rainforest canopy) A_max=50 dB, gamma=0.35
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The constant gets multiplied by sqrt(f_GHz / 1) for frequency scaling.
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"""
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params = {
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"sparse": (20.0, 0.10),
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"moderate": (35.0, 0.20),
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"dense": (50.0, 0.35),
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}
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a_max, gamma = params[foliage_density]
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freq_scaling = np.sqrt(freq_ghz) # higher freq → more attenuation
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return a_max * freq_scaling * (1.0 - np.exp(-gamma * distance_m))
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def esp32_link_budget(freq_ghz: float) -> dict[str, float]:
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"""ESP32-S3 1x1 SISO link budget at 2.4 / 5 GHz.
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Numbers from Espressif ESP32-S3 datasheet + standard WiFi specs:
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Tx power (max regulatory) +20 dBm (100 mW, FCC Part 15)
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Tx antenna gain (PCB) +2 dBi
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Rx antenna gain (PCB) +2 dBi
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Rx sensitivity (HT20, MCS0) -97 dBm
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Total link budget (free-space) = (20 + 2 + 2) - (-97) = 121 dB
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"""
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return {
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"tx_power_dbm": 20.0,
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"tx_gain_dbi": 2.0,
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"rx_gain_dbi": 2.0,
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"rx_sensitivity_dbm": -97.0,
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"link_budget_db": 121.0,
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}
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def fspl_db(freq_ghz: float, distance_m: float) -> float:
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"""Free-space path loss in dB. FSPL = 20·log10(4π·d/λ)
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With f in GHz + d in m: FSPL = 32.45 + 20·log10(f) + 20·log10(d)"""
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if distance_m <= 0: return 0.0
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return 32.45 + 20 * np.log10(freq_ghz) + 20 * np.log10(distance_m)
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def max_sensing_range(freq_ghz: float, foliage_density: str, snr_margin_db: float = 10.0) -> float:
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"""Distance at which FSPL + foliage_attenuation = link_budget - snr_margin.
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Numerical solve by binary search. Returns metres."""
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lb = esp32_link_budget(freq_ghz)
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budget = lb["link_budget_db"] - snr_margin_db # require SNR > snr_margin
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lo, hi = 0.1, 1000.0
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for _ in range(60):
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mid = (lo + hi) / 2
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total_loss = fspl_db(freq_ghz, mid) + itu_p833_attenuation(freq_ghz, mid, foliage_density)
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if total_loss < budget:
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lo = mid
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else:
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hi = mid
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return (lo + hi) / 2
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def gait_frequency_band(species: str) -> dict[str, float]:
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"""Approximate gait stride-frequency bands per species class, from
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biomechanics literature (Schmitt 2003, Gambaryan 1974, Heglund 1988).
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These are the temporal frequencies a CSI motion-band filter would
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target — for context, human walking is ~1.7 Hz, jogging ~2.5 Hz."""
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bands = {
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"human-walking": {"min_hz": 1.2, "max_hz": 2.5},
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"deer": {"min_hz": 1.8, "max_hz": 4.0},
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"wolf": {"min_hz": 1.5, "max_hz": 3.5},
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"bear": {"min_hz": 0.5, "max_hz": 1.5},
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"fox": {"min_hz": 2.0, "max_hz": 4.5},
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"squirrel": {"min_hz": 4.0, "max_hz": 10.0},
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"mouse": {"min_hz": 5.0, "max_hz": 15.0},
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"raccoon": {"min_hz": 1.5, "max_hz": 3.5},
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"wild-boar": {"min_hz": 1.0, "max_hz": 2.5},
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"elk": {"min_hz": 1.5, "max_hz": 3.0},
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}
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return bands.get(species, {"min_hz": 0.5, "max_hz": 10.0})
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="examples/research-sota/r10_foliage_results.json")
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args = parser.parse_args()
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distances = np.array([1, 2, 5, 10, 20, 50, 100, 200], dtype=np.float64)
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freqs = [2.4, 5.0]
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densities = ["sparse", "moderate", "dense"]
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curves = {}
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for freq in freqs:
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curves[str(freq)] = {}
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for density in densities:
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atts = [float(itu_p833_attenuation(freq, d, density)) for d in distances]
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fspls = [float(fspl_db(freq, d)) for d in distances]
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curves[str(freq)][density] = {
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"distance_m": distances.tolist(),
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"foliage_attenuation_db": atts,
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"fspl_db": fspls,
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"total_loss_db": [a + f for a, f in zip(atts, fspls)],
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}
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# Max sensing range per (freq, density)
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max_ranges = {}
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for freq in freqs:
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max_ranges[str(freq)] = {d: float(max_sensing_range(freq, d)) for d in densities}
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species_gaits = {s: gait_frequency_band(s) for s in
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["human-walking", "deer", "wolf", "bear", "fox",
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"squirrel", "mouse", "raccoon", "wild-boar", "elk"]}
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out = {
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"model": "ITU-R P.833-9 specific-attenuation + free-space-path-loss",
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"link_budget": esp32_link_budget(2.4),
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"snr_margin_db": 10.0,
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"curves": curves,
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"max_sensing_range_m": max_ranges,
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"species_gait_bands_hz": species_gaits,
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}
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Path(args.out).parent.mkdir(parents=True, exist_ok=True)
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Path(args.out).write_text(json.dumps(out, indent=2))
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print("=== ESP32-S3 through-foliage sensing range (link budget 121 dB, 10 dB SNR margin) ===")
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print(f"{'freq (GHz)':>10} {'sparse':>9} {'moderate':>11} {'dense':>9}")
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for freq in freqs:
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row = f"{freq:>10.1f} "
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for d in densities:
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row += f"{max_ranges[str(freq)][d]:>9.1f}m " if d != "moderate" else f"{max_ranges[str(freq)][d]:>11.1f}m "
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print(row)
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print()
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print("=== Per-species gait frequency bands (Hz) ===")
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for s, b in species_gaits.items():
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print(f" {s:<16} {b['min_hz']:.1f} - {b['max_hz']:.1f} Hz")
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print()
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print(f"Wrote {args.out}")
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if __name__ == "__main__":
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main()
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@ -0,0 +1,323 @@
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
Loading…
Reference in New Issue