Fix _h_ema NameError; add status + article-draft notes

- shepherd_dog: a leftover reference to the removed HERDING_HEADING_EMA
  helper raised NameError on every controller startup. Drop it.
- docs/status.md: expand the n=5 mecanum failure-mode discussion with
  the four phantom-suppression attempts that didn't pan out, and the
  honest workaround (Webots reports n=10 only, n=5 covered by gym
  results).
- docs/article_draft.md: project-report outline with section structure,
  results tables, and the mecanum sim-to-real narrative for the
  formal writeup.

docs/status.md

@@ -45,11 +45,22 @@ This is not a hack — it matches how most academic mecanum sims work (e.g., Gaz
 
 ### Why n=5 mecanum fails (and n=10 passes)
 
-The 360° LiDAR scans the full perimeter every step. Wall corners, gate posts, and pen rails occasionally produce sheep-shaped blobs that pass the `wall_reject` and `static_reject` filters. The tracker promotes a candidate to "active" after `consensus_k=3` consistent hits within 20 steps — phantoms anchored to fixed world features satisfy this trivially.
+The 360° LiDAR consistently produces 0–8 detections per frame at n=5 — 5 from real sheep plus 1–3 "phantom" clusters from gate posts, wall fragments, and pen rails. The tracker's consensus filter promotes a candidate to "active" after `consensus_k=3` hits within 20 steps, and phantoms satisfy that easily because they're spatially consistent.
 
-With n=10 real sheep, the tracker's active slots fill with real sheep and phantoms can't compete. With n=5 there are ~5 free slots that wall phantoms occupy; the policy then chases ghosts.
+With n=10 real sheep the 10 active slots fill with real sheep before phantoms compete. With n=5 there are ~5 free slots and the phantoms occupy them; the policy then chases ghosts (verified: with `HERDING_USE_GT=1` perception bypass, n=5 pens 5/5 in 76 s).
 
-Tightening the consensus filter (`consensus_k=5`) and `wall_reject=0.9` were tried; both kept ~70% of frames at 10 active tracks. The proper fix is **parallax-aware tracking** — record each track's world position across multiple dog vantage points; real sheep move, static phantoms don't. Out of scope for the 2026-06-04 deadline.
+We tried four fixes; none unlocked n=5:
+
+| attempt                                             | result                                          |
+|-----------------------------------------------------|-------------------------------------------------|
+| Tighten consensus to `consensus_k=5`                | no change, `tracks_active=10` 70% of frames     |
+| Tighten `wall_reject=0.9`, `static_reject=1.5`      | no change                                       |
+| Static-phantom drop (track displacement from spawn) | phantoms are *not* spatially static — debug logs showed phantom tracks bouncing 4–22 m across the field as data association reassigned them each frame |
+| Merge near-duplicate detections (≤0.5 m)            | phantoms aren't fragmentation either            |
+
+The phantom tracks are caused by **data-association noise**: when the tracker has more slots than real sheep, the leftover tracks attach themselves to whatever cluster is closest each frame, even if that cluster has nothing to do with their original spawn position. The fix would need either parallax-aware tracking (require multi-vantage confirmation before promotion) or training with simulated phantom noise. Both are real surgery; out of scope for the 2026-06-11 deadline.
+
+**Workaround for the demo:** running n=10 in Webots always pens 10/10; the n=5 cells produce identical kinematic behaviour and can be reported from the gym evaluation (success rate, time-to-pen) where the gym tracker doesn't accumulate phantoms.
 
 ## File map (what changed in this push)