R10 — Through-foliage wildlife sensing: physics-grounded feasibility

Status: physics + per-species gait taxonomy landed · 2026-05-22

The 10-20 year vision

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.

This thread does the physics feasibility check, the per-species gait taxonomy, and the bounded honest range estimates that any real deployment would need.

Through-foliage propagation (ITU-R P.833-9)

Vegetation attenuation is modelled as A_v(d) = A_max · (1 − e^(−γd)) · √f:

Foliage density	A_max	γ
Sparse (orchard, savanna)	20 dB	0.10 m⁻¹
Moderate (suburban tree cover)	35 dB	0.20 m⁻¹
Dense (rainforest canopy)	50 dB	0.35 m⁻¹

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:

Tx power (FCC max):          +20 dBm
Tx antenna (PCB):            +2 dBi
Rx antenna (PCB):            +2 dBi
Rx sensitivity (HT20 MCS0): -97 dBm
                            ─────
Total link budget:          121 dB
SNR margin for CSI DSP:     10 dB
Usable budget:              111 dB

Bounded sensing range

examples/research-sota/r10_foliage_attenuation.py solves for the distance at which FSPL + foliage_attenuation = 111 dB:

Frequency	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 — 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.

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.

Per-species gait-frequency taxonomy

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:

Species	Stride frequency (Hz)	DSP filter
Bear, sloth, wild boar	0.5 – 1.5	low-band
Human walking	1.2 – 2.5	mid-band
Elk, raccoon, wolf	1.5 – 3.5	mid-band
Deer	1.8 – 4.0	mid-band
Fox	2.0 – 4.5	mid-band
Squirrel	4.0 – 10.0	upper-band
Mouse, songbird	5.0 – 15.0	upper-band

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.

What this depends on

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.

The pose-estimation infrastructure already exists; only the labels change.

What this DOES enable today

Even without species classification:

Presence + count. The cog-person-count v0.0.2 retrained on a generic "thing moving in foliage" dataset would already work, no architecture changes.
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.
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.

Honest scope

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.
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.
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.
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).

What this DOES NOT prove

That a specific species can actually be ID'd from CSI alone in field conditions.
That solar + LiPo can sustain 24/7 capture in low-light forest environments.
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.)

Vertical applications (10-20 year)

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.
Wildlife corridor verification. Solar-powered ESP32 nodes along a corridor confirm whether transboundary migrations are actually happening.
Invasive-species early warning. Per-species gait classifier flags first arrival of new species in a watershed.
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.
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).
Pest control — automated detection of mouse / squirrel populations in agricultural storage facilities.

Connection back

R5 (saliency) — per-species classifiers would need their own saliency maps; the count-saliency may not transfer. Same task-specific issue surfaced in R12.
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.
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.
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.

What's next on this thread

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.
Camera + ESP32 dual capture in a backyard with the bird feeder visible — small-scale labelled wildlife dataset for the proof-of-concept.
ADR for "wildlife sensing cog" — same cog-* packaging, different model, different data, identical deployment story. Could ship as cog-wildlife once labelled data exists.