feat: add per-frame 2D visualization (static demo)

[YoshiRi]

src/tracking/evaluation/interactive.py

• _ggiw_to_ellipse(): GGIW extent → {ext_a, ext_b, ext_theta, x, y, vx, vy}
• run_once(): add record_frames flag; captures GT/obs/est snapshots per frame
• run_simulation_json(): include frame_data (sensor_origin + frames[]) from MC run 0

scripts/generate_demo.py

• Runs run_simulation_json for selected/all scenarios
• Writes docs/data/demo_.json

docs/demo.html

• Canvas 2D renderer: GT rectangles (green), LiDAR points (yellow),
  GGIW estimate ellipses (red), velocity arrows, sensor origin marker
• Frame slider + Play/Pause/Step/Reset controls
• Auto-fit coordinate transform with grid and axis labels
• Missed-detection badge for miss frames
• Per-scenario summary metrics strip
• Loads all scenarios from docs/data/ via fetch (falls back gracefully)

tests/tracking/test_interactive.py (+10 tests)

• frame_data present and correctly nested
• frame index sequential, GT fields valid, obs pairs finite
• est ellipse axes positive (guards eigenvalue sign bugs)
• missed frames have empty obs + missed=True flag
• record_frames=False → snapshots is None
• record_frames=True → snapshots length matches n_frames

Usage (local preview):
uv run python scripts/generate_demo.py --all
cd docs && python -m http.server 8080

open http://localhost:8080/demo.html

https://claude.ai/code/session_013sQREGvTmjgFT8cyRNmPUL