Touchdown Localisation with aircraft flight data
summary
Parses QAR/FDR binary into CSV, uses Python (pandas, GeoPandas, folium, overpy, shapely) to align radio-altimeter profiles with runway geometry and produce ground tracks and density estimates. Implements radio altimeter spline fitting and map matching against OpenStreetMap, SRTM and Ordnance Survey surfaces, processing examples of 219 flights from a ~18 TB archive and noting a full 400,000-flight dataset. Delivers touchdown offsets ranging roughly −200 m to +300 m and a working pipeline that improves localization but requires formal error quantification and cross‑airport validation.
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chapters (25)
- 0:00Touchdown localisation research
- 1:10Precise touchdown in aviation
- 2:35Flight data sensors
- 5:43MATLAB to Python
- 6:51Flight data variables
- 7:54Detecting touchdown with accelerometer
- 9:38Pilot interaction with controls
- 10:20Geospatial touchdown visualisation
- 12:27Terrain profiles with radar altimeter
- 13:52Aligning terrain data
- 15:40Flight trajectories overlay
- 16:27Touchdown distribution density
- 17:12Large datasets with multiprocessing
- 18:57Taxiway data validation
- 20:43Ground vs airspeed differences
- 21:25Aircraft trajectories with GeoPandas
- 24:02Final touchdown results
- 25:07Terrain model expansion
- 27:36Taxiway alignment validation
- 28:17Summary and future research
- 28:43Q&A: Data noise
- 30:50Q&A: OpenStreetMap library
- 31:32Q&A: Data validation
- 32:38Q&A: Radar limitations
- 33:42Q&A: Safety insights
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