--- license: cc-by-4.0 task_categories: - object-detection - audio-classification tags: - drone-detection - counter-uas - acoustic-array - ptz-tracking - multimodal - gps-truth - low-altitude pretty_name: "nightjar field flight 2026-08-04 #4 — longest tracked sortie of the day" --- # nightjar flight — 2026-08-04, fourth sortie Last sortie of a four-flight day, and **the one with the most time under an optical track**. Same rig and site as the other 2026-08-04 sessions. GPS truth: **3,572 valid UTC stamps of 3,573**. | | | |---|---| | airborne | 357 s | | max height | 26 m | | max slant range | 244 m | | median elevation angle | **5°** | | path flown | 894 m | ## The interesting number **53 cue candidates, and 43 of them were evaluated at the 14.6 dB threshold.** The deployed presence gate is state-dependent — strict (14.6 dB) while nightjar reports TRACK, relaxed (10.0 dB) while it is searching — so a candidate stamped 14.6 means the tracker *was locked at that moment*. Only 10 candidates saw the relaxed threshold. Compared with the third sortie, where all 8 candidates were at 10.0 dB and the tracker was never locked when a cue was evaluated, this session spent substantially more of its life holding a track. That is a suggestive contrast, not a controlled one: the two sorties differ in duration (357 s vs 200 s), range (244 m vs 142 m) and path length (894 m vs 357 m) as well. `audio/cue_log.jsonl` stamps `thr_db` and `nj_state` on every candidate, so each cue is attributable to the threshold in force. 4 chirp-rejects. Two `start` records appear because the cue daemon was restarted into the session after a device-contention race at launch (the daemon *is* the UMA recorder — the array is single-open). ## Caveat that applies to the whole day ⚠ **26 m altitude at 244 m range is a ~5° elevation angle** — the drone was on the treeline, against foliage rather than sky. Three of the day's four sorties flew this profile. It is a hard background for the detector and it sits at the null of a horizontal array's elevation response (sensitivity goes as sin(el), so ~0 at the horizon). The 50–150 m detection question that motivated the day's work therefore remains unexercised. ## Contents / gotchas ``` audio/uma16_.wav UMA-16, 16 ch, S16_LE audio/sb_.wav SB-POLARIS, 8 ch, S32_LE audio/cue_log.jsonl cue gate log (thr_db, nj_state per candidate) video_segments.tar 1080p native + 768x432 substream, 60 s MPEG-TS blackbox/state.jsonl tracker FSM ~2.9 Hz blackbox/frames.tar 2,856 stills f.jpg dji/*.csv, *.txt decoded + original FlightRecord (CUSTOM.dateTime = true UTC) ``` UMA-16 0-based channel 9 is a dead electronic-floor channel — exclude it, run on 15. SB-POLARIS has 3 live capsules of 8; detect them by RMS (live ~−70 dBFS, dead ~−190) rather than hardcoding. ⚠ **The arrays use different sample widths** — UMA S16_LE, SB S32_LE; reading both as int16 scrambles SB channel identity and destroys its spectrum. Calibration as flown: `az_offset_deg` **301.37**, `invert_az` true. Cue producer: cheap daemon (`SUBHUNT=0`). Site notch 100–200 Hz. Start pose pan 178.9 / tilt 45, pointed at the JBL beacon. Cue-gated blob seeding was present in the build but **disabled** for this flight. ## GPS-referenced pixel work Placing the drone in the image from GPS reaches a floor of about **5° of bearing error** on this rig's data — the DJI's own ±2–5 m of position is 4–9° of bearing at the 20–45 m ranges where the optical correspondences live. Treat any in-frame or pixel-level claim derived from GPS here as approximate. ## Elevation datum (fit_el_datum, 2026-08-16) The rig height in this day's drone-altitude frame (the missing constant that floored close-range elevation truth at ~25 deg estate-wide) is SOLVED from this repo's own blackbox TRACK/LOCK poses vs the eval-cache GPS — no acoustics, no field survey. - **z_rig = +1.70 m**, el = -1 x tilt + 75.38 deg - north offset -64.56 deg (pan sign -1), az gate residual 3.4 deg - med |el residual| **1.10 deg** over 187 camera-on-drone rows, horiz 12-14 m - per band: 0-30m: 1.10 deg (n=187) Artifact: `joshruby/acoustic-knowledge` -> `v3/assets/el_datum/nightjar-flight-20260804-4.json`. Tool: `sirch613/subhunt-v2` `v3/fit_el_datum.py`. Derive el truth as atan2(hgt - z_rig, horiz) — NEVER from raw hgt.