Calibration of Hubble Space Telescope Focal-Length Variations Using the Embedding Technique

Details Calibration of Hubble Space Telescope Focal-Length Variations Using the Embedding Technique Calibration of Hubble Space Telescope Focal-Length Variations Using the Embedding Technique

Jun 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001flme.symp..485b&link_type=abstract

2001 Flight Mechanics Symposium, p. 485

Computer Science

Performance

Calibrating, Computerized Simulation, Focusing, Hubble Space Telescope

Scientific paper

A modeling method that allows one to rapidly build data-driven models of nonlinear components is discussed. The models are constructed from input/output time domain data and their 'embeddings'. The notion of models built from embedded data is described in the Taken's Embedding Theorem and has been extensively explored for modeling systems in the physics literature. The authors from Agilent Laboratories are developing practical methods to extend these results to non-autonomous systems by creating tools that allow engineers to rapidly build models for driven nonlinear components. These models can be used in simulation, process control, diagnostics, and sensor calibration. Using these methods a 'black-box' data-driven model is generated to calibrate Hubble Space Telescope (HST) focal-length changes on a 5-minute time grid for the period from 1995-1999. These models are built using a program, CHAOS, developed by Agilent Laboratories. The data-driven model predicts the focus for the measured points about 36% better than the Full-Temperature Model (FTM) constructed from a detailed knowledge of the telescope structure. As demonstrated by this HST focal-length calibration, data-driven models, such as those generated with the CHAOS package, have great potential for application to a wide spectrum of HST/Next Generation Space Telescope (NGST) calibration problems. In particular, for sensor calibration applications, blackbox nonlinear models can be generated rapidly, which have similar or better performance than models built from a detailed understanding of the system structure.

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