decide op• Data kinds: signal → table
• Call: import fullseye as fs; fs.ledger.doppler_track(signal, fs, *, f_high: 'float' = 960.0, f_low: 'float' = 770.0, c: 'float' = 343.0, smooth: 'float' = 0.005, guard: 'float' = 0.01, edge: 'float' = 0.05, v_tol: 'float' = 1.0, verdict_window: 'float' = 0.5) -> 'Dict[str, object]' (to call the implementation directly, import drivedecide; drivedecide.doppler_track(signal, fs, *, f_high: 'float' = 960.0, f_low: 'float' = 770.0, c: 'float' = 343.0, smooth: 'float' = 0.005, guard: 'float' = 0.01, edge: 'float' = 0.05, v_tol: 'float' = 1.0, verdict_window: 'float' = 0.5) -> 'Dict[str, object]'; from the registry, opsdrive.get("doppler_track"))
Estimates the instantaneous frequency from the siren sound and determines whether it is approaching or receding.
1. Phase difference of the analytic signal (Hilbert transform via FFT) → instantaneous frequency f(t) = arg(z[n+1] z̄[n]) fs / 2π.
2. Moving median of width smooth seconds.
3. At each time, of the 2 nominal tones (f_high / f_low) the one with the closer ratio is taken as "the emitted tone"; Doppler factor D = f / f_nom, line-of-sight velocity v_r = c (1 − 1/D) (doppler_shift solved for v_r).
4. guard seconds before and after a tone switch (jump of the median > 20 Hz) and edge seconds at both ends are invalid (valid = False).
5. Verdict: median of the valid v_r over the last verdict_window seconds greater than +v_tol → "approaching", less than −v_tol → "receding", in between → "abeam". state is the same verdict at each time ("unknown" where invalid).
Limit of mistaking the nominal tone: the dividing line for assignment is the geometric mean √(f_h f_l). The low tone crosses it when approaching at v_r = c (1 − √(f_l/f_h)), the high tone crosses it when receding at v_r = −c (√(f_h/f_l) − 1). For 960/770 Hz these are +35.8 m/s (129 km/h) and −40.0 m/s. Faster line-of-sight velocities are misassigned (fixed in the tests).
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_driving_decisions — py -3.11 examples/poc_driving_decisions.py
table as input)course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same
decide)mirror_reflection_matrix · mirror_virtual_camera · mirror_aim_normal · convex_mirror_fov · mirror_blind_zone · check_sequence_score · signal_phase_plan · signal_state
*Provenance: drivedecide.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.