mjshiggins/dopplertrack

选用指南

AI 根据 README 整理 · 2026/11/4 08:22:22

使用方式

通过命令行使用，提供多个子命令如 spectrogram、detect、fit、fetch-day、search 和 refine。

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(emission-time radial velocity) and score it against the spectrogram. Event 1's placement absorbs measurement error; event 2's placement is locked to the flight geometry. 5. `refine` — 50 m grid around a winning candidate. Further detected flyovers can be added here: each is matched to a flight once, at the candidate, then scored at every fine point with the same time shift as event 1, so every event is locked to one clock. ## Usage ```bash uv sync uv run dopplertrack spectrogram recording.mp4 rec.npz

部署要求

需要 Python 环境，使用 uv 进行依赖管理；需要 ffmpeg 解码音频/视频；需要访问 adsb.lol 的 GitHub 存档、ICAO Doc 8643 和 AWS Terrain Tiles 等数据源。

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over a 100 x 150 km region ranks the true location first, 43 m off after refinement. On a 12-hour public eagle-cam stream (North Fort Myers, FL, 2024-12-28), with the flyovers found automatically, the search ranks the right pair of flights first of 607 and places the camera within 600 m. See Results below. ## Pipeline 1. `spectrogram` — decode any audio/video with ffmpeg, high-resolution STFT of the 40–450 Hz band, whitened per frequency bin so only *changing* tones stand out. 2. `detect` — find the flyovers without a person: slide a window over the

whole recording, grid-search an ideal straight-pass Doppler curve (f0, speed, CPA slant distance, CPA time) in each, keep the local maxima, refine them and rank them. `fit` does the same for one window you choose. The curve parameters only seed a search with coarse ranges. 3. `fetch-day` — stream one day's [adsb.lol](https://github.com/adsblol/globe_history_2026) archive straight from GitHub (4 GB, nothing written to disk) and keep the flights that touched a bounding box, dropping known jets using ICAO Doc

observation-time), propagation-delayed CPA times, slant distance with a DEM, the inter-event offset filter, the silence filter, and locked two-event scoring where event 1 absorbs measurement error and event 2 is placed purely by flight geometry. ## Data sources (all free) | Need | Source | |---|---| | Historical ADS-B | adsb.lol `globe_history_YYYY` GitHub releases (~4 GB/day) | | Aircraft engine class | OpenSky mirror of ICAO Doc 8643 | | Elevation | AWS Terrain Tiles (Terrarium) | ## Development ```bash uv run pytest ``` ## Releasing

已知限制

作者指出，当飞机几乎在头顶上方时，定位精度会降低，因为斜距变化不大；增加更多飞越事件可以提高精度。

查看 README 依据

| Margin over the best wrong pair | 2.5 % | | Top candidate, 200 m grid, two events | 917 m from the nest | | Refined peak, 50 m grid, two events | 1.7 km from the nest | | Refined peak with three more detected flyovers | 592 m from the nest |  The right flights are identified, but two events pin the location only to about 2 km here, and the reason is geometry rather than noise: both aircraft passed nearly overhead at 2.5 km and 5.7 km altitude, so the slant distance that shapes the Doppler curve barely changes as the observer moves

a kilometre on the ground. At Oyster Bay the King Air passed 3.5 km to the side at 1 km altitude, which is why that peak is sharp. Adding three more flyovers found by `detect` (`refine --extra`), locked to the same clock, brings the peak to 592 m. Lower, lateral passes are what pin a location, so the more flyovers a long recording offers, the better. Numbers: `docs/results/swfl_search_best_per_pair.csv`, `docs/results/swfl_refined_50m.csv`, and the detector's candidates for both recordings in `docs/results/*_detected.json`.