development of automated slr data processing at mt stromlo ... · ranging server. •treats...
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Development of Automated SLR Data Processing at Mt Stromlo SLR Station
Christopher MooreEOS Space Systems Pty Ltd
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Automated Post Processing
❑ What?
Automatically...
o input files containing CSPAD timing event epochs from ranging targets.
o separate ranging measurements (signal) from noise.
o extract/apply system calibration data.
o generate output files including full rate and normal points.
❑ Why?
To…
o reduce (eliminate?) manpower requirements
o improve responsiveness
o improve quality and consistency of results
o improve productivity2
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❑ Manage system (pre- and post-) calibration data and apply
them to SLR data in a reliable and consistent way.
❑ Identification of satellite returns in low S/N conditions and elimination of noise
without
o Significant signal data loss
o Including noise (false positives)
❑ Developing a complex system consisting of many system components.
Challenges
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❑ Managing system (pre- and post-) calibration data and applying them to SLR
data in a reliable and consistent way.
❑ Identify satellite returns in low S/N conditions and eliminate
noise without
o Significant signal data loss
o Including noise (false positives)
❑ Developing a complex system consisting of many system components.
Challenges
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Examples of typical ranging data.
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❑ Managing system (pre- and post-) calibration data and applying them to SLR
data in a reliable and consistent way.
❑ Identification of satellite returns in low S/N conditions and elimination of noise
without
o Significant signal data loss
o Including noise (false positives)
❑ Develop a complex system consisting of many system
components.
Challenges
6
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Mt Stromlo SLR System
Software modulessupporting autonomous range data processing
Site Data
Interface
Post Processing
Server
Site DataEditor
Target DataEditor
System Calibration
Interface
System Calibration Data Editor
Brd DataAnalysis Library
Target DataInterface
Manual Processor
Profits
Pass SummaryInterface
Pass Summary
Editor
BRD File Server
BRD File Data
Interface
Track/Track Generator
ProcessingParameterization
Auto Post Processor
Report Generator
Report Manager
Pass SummaryDatabase
XML filesBRD files
Operation Reports
NP, FR files
Site Database
Target Database
SystemCalibration
Database
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Post Processor Characteristics
• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by the system
ranging server.
• Treats calibration target data similar to SLR data and applies the
necessary system calibrations.
• Uses a configurable recipe of various data filtering and curve fitting
procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal point, mets and
pointing data.
• Independent generation/publication of reports (CRD etc).
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Post Processor Characteristics
• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by the system
ranging server.
• Treats calibration target data similar to SLR data and applies the
necessary system calibrations.
• Uses a configurable recipe of various data filtering and curve fitting
procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal point, mets and
pointing data.
• Independent generation/publication of reports (CRD etc).
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Post Processor Characteristics• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by
the timing system and ranging server.
• Treats calibration target data similar to SLR data and applies the
necessary system calibrations.
• Uses a configurable recipe of various data filtering and curve fitting
procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal point, mets and
pointing data.
• Independent generation/publication of reports (CRD etc).
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Binary Range Data files (*.BRD)
❑Captures raw data from the ranging server, including;o Pass metadata (System , Target, Track data)
o Shot Events
o Mets, Cloud data
o Telescope Pointing
o Prediction Element(s)
o System State/Interlocks
❑Stored as serialized files using Google’s Protocol Buffers.
o Compact (<50% of binary files.)
o Efficient ( Supports fast processing.)
o Schema based( Supports backward compatibility. )
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Post Processor Characteristics• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by the system
ranging server.
• Treats calibration target data similar to SLR data and
applies the necessary system calibrations.
• Uses a configurable recipe of various data filtering and curve fitting
procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal point, mets and
pointing data.
• Independent generation/publication of reports (CRD etc).
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Processing Logic
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Post Processor Characteristics• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by the system
ranging server.
• Treats calibration target data similar to SLR data and applies the
necessary system calibrations.
• Uses a configurable script of various data filtering
and curve fitting procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal point, mets and
pointing data.
• Independent generation/publication of reports (CRD etc).
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Post Processor Characteristics• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by the system
ranging server.
• Treats calibration target data similar to SLR data and applies the
necessary system calibrations.
• Uses a configurable recipe of various data filtering and curve fitting
procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal point, mets and
pointing data.
• Independent generation/publication of reports (CRD etc).
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Processing Recipe
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Processing MethodsOptimized Time Bias Performs a recursive time bias scan to maximize the histogram peak. Identifies possible signal.
Noise Filter Uses rectangular histogram bins (2 at a time) in time slices to remove as much sparse data as possible.
Poisson Filter Uses rotated trapezoidal histogram bins (2 at a time) in time slices to identify probable signal and remove remaining
noise. Based on Ricklefs and Shelus (1993), 8th International workshop on Laser Ranging Instrumentation.
Polynomial Filter Uses polynomials of increasing degree to filter points outside a band around the fitted curve. This technique works
well if sparse noise has been removed and the signal dominates the remaining data points.
Polynomial Fit This selects the best polynomial to fit to the accepted data such that the polynomial degree does not exceed the
maximum allowed, determined using the Akaike Information Criteria to avoid over-fitting. See Akaike H., 1974, IEEE
Transactions on Automatic Control 19 (6): 716–723.
Orbital LS Fit Undertakes a least squares fit to all the accepted data to minimize range bias and time bias. It is most effective when
signal dominates noise. This method is applied once noise is removed to estimate range and time bias and to reset
any fitting function.
Iterated Mean Filter Used when the residual data is expected to be constant and distribution is probably non-Gaussian. During iteration,
data on the wings of the distribution may be filtered. See Sinclair (1995) Proceedings of Annual Eurolas Meeting,
Munich.
RMS Filter Used as a final check for excessive spread of data within time bins.
Normal Point Generator Generates normal point data according to the ILRS normal point algorithm (Sinclair, 1997).
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Post Processor Characteristics• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by the system
ranging server.
• Treats calibration target data similar to SLR data and applies the
necessary system calibrations.
• Uses a configurable recipe of various data filtering and curve fitting
procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal
point, mets and pointing data.
• Independent generation/publication of reports (CRD etc).
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Post Processor Characteristics• Supports manual and automated processing.
• Input is binary range data (BRD) files generated by the
system ranging server.
• Treats calibration target data similar to SLR data and
applies the necessary system calibrations.
• Uses a configurable recipe of various data filtering and
curve fitting procedures.
• Uses configurable normalized parameters.
• Output is an XML file of meta-data, full rate, normal
point, mets and pointing data.
• Independent generation/publication of
reports (CRD etc).19
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Processed files managed by the Data Distribution Server
Autonomous Report Generation and Data Management
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Current Status
❑ On-going analysis and regression testing, including
o Differential analysis of 20000+ manually and automatically
processed BRD files.
o Identification and focus on outliers, especially false positives.
o Minimization of data loss.
o External independent analysis.
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Current Status
❑ Productivity 90-95% vs manual.
❑ False positives approaching acceptable level.
❑ Manual processing hand-off for special
targets or inconclusive results.
❑ Supports evolutionary improvement.
❑ Adoption at Mt Stromlo imminent.
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