AIAA ANERS Radar Trajectory Processing Technique for Merged Data Sources. April 21, 2017 Prepared by Bao Tong. Federal Aviation Administration

Transcription

1 AIAA ANERS 2017 Administration Radar Trajectory Processing Technique for Merged Data Sources April 21, 2017 Prepared by Bao Tong Federal 0 Aviation Administration 0

2 Introduction The FAA has access to multiple historical radar data sources TFMS, PDARS, NOP, etc. Method needed for unifying how noise, fuel burn, and emission are computed based on actual radar tracks AEE internal analyses NextGen benefits analyses Raw radar data should not be used directly in AEDT s performance-based methods Assumptions and data quality concerns Multi-sensor fusion of data into single flight track not trivial External pre-processor has been developed to identify and resolve common issues Big data analytics techniques applied to facilitate interactive, geospatial queries for real-time data analysis/exploration Administration 1

3 Radar Data Quality Issues Spatial uncertainties (lat/long/alt) Tends to increase with range from ground-based interrogators Ground reflection (ghosting) issues Pressure altitude vs. geometric altitude Temporal uncertainties Time synchronization between facilities Interlacing good data with bad Strong impact on ground speed calcs Additional post-processing needed Requires understanding of data flow and intermediate processing steps from each data source provider Completion of flight tracks in presence of data gaps Merging track data to preserve consistency in ground-speed estimation Administration 2

4 Uncertainty in Ground Speed Profile Visually smooth ground track not a good indicator due to time sync between facilities ASDE-X/TRACON/CENTER has sweep rates of around 1s/5s/12s, respectively Spurious oscillations in ground speed indicates poor data quality (supersonic speeds?) Moving time-average possible, but leads to synchronization issues with corresponding ground track points and altitude especially for long flight performance computations Merged radar data can result in worse performance than its individual inputs Merging process needs to consider facility hand-off messages Administration 3 3

5 Uncertainty in Altitude Profile Pressure-altitude reported by aircraft altimeter and relayed to the ground Below 18k ft, altimeter calibrated to local ground conditions Shape of data indicates vertical offset No definitive answer regarding ASDE-X altitude being pressure altitude or geometric altitude Non-physical altitude jump of ft visible on ASDE-X on ground Data transitions need to be declared and traceable to original data source Different sensors have different jitter/noise characteristics Method needed for consistent data source prioritization/merging Vertical offset between TRACON/ASDE-X ASDE-X altitude not reliable on ground GPS-based data source for same flight track needed for further validation Administration 4

6 Radar Data Processing Technique Comparison of ground tracks before and after applying smoothing spline on NOP data (gold curve can be simplified with 2D RDP) Ground speed processing via down-sampling and clustering results to reduce ambiguity in estimated ground speeds Altitude profile can be represented with tens of points due to simple shape (via 1D RDP) Linear model used between change-points identified to resample data as needed Administration 5

7 Validation and Verification DISCOVER-AQ (2014 Denver) Compared INS+GPS to processed radar Ground track offset Method also applicable to GPS-based data sources Linkages with BADA 4 and AEDT sensor path development Integrate w/ weather data Administration 6

8 Big Data Visualization and Geo-spatial Queries High performance data format as storage back-end for analytic work flows Hierarchal Data Format (HDF5) compatibility w/ modeled weather data xarray (python) for multi-dimensional inmemory arrays + manipulations Datashader (python) enables generation of heat maps for billions of points in real-time Fast, parallelized method for data aggregation and transformations Automated network detection to obtain backbone tracks, compute metrics, etc. One month KBOS data visualization One month KBOS data below 500 ft Administration 7

9 Web Analytics Platform (Under Development) Platform needed to manage, filter, and select desired data for further analysis Repository for storing processed radar data and serving requests Web app enables quick data access and retrieval from trusted machines (intranet) Provides interactive front-end to easily visualize multi-dimensional data Data + analysis can be done remotely through a browser window Administration 8

10 Summary and Next Steps Common issues with radar-based data sources examined Method for data simplification identified and validated in 4D Excellent data reduction capabilities while preserving track features Change-point analysis enables 4D aircraft trajectory to be resampled to capture weather fluctuations and fill-in data gaps More extensive validation against different aircraft types needed (e.g., business jets, commercial, military, etc.) Refining weather data in 4D based on simplified physics needed in APM to properly model environmental operating conditions Needed for determining True Air Speed (TAS), engine states, Mach number, etc. Administration 9

11 Questions? Comments? Suggestions? Administration 10