Incorporation of a positivity constraint into a Kalman-filter-based algorithm for correction of spectrometric data

Improving the resolution of spectrometric analyses by numerical processing of spectrometric data subject to systematic errors of an instrumental type, as well as to random errors, is addressed. It is assumed that the model of the spectrometric data has the form of an integral, convolution-type equat...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 44; H. 1; S. 2 - 7
Hauptverfasser: Massicotte, D., Morawski, R.Z., Barwicz, A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.02.1995
Institute of Electrical and Electronics Engineers
Schlagworte:
Absorption
Algorithm design and analysis
Computers in experimental physics
Data analysis: algorithms and implementation; data management
Error correction
Exact sciences and technology
Instruments
Instruments, apparatus, components and techniques common to several branches of physics and astronomy
Integral equations
Light sources
Mass spectroscopy
Optical filters
Optical recording
Physics
Spectroscopy
Algorithms
Experimental data
Numerical computation
Spectral resolution
Signal-to-noise ratio
Data processing
Error correction
Experimental study
Kalman filters
Constrained optimization
ISSN:0018-9456
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Zusammenfassung:Improving the resolution of spectrometric analyses by numerical processing of spectrometric data subject to systematic errors of an instrumental type, as well as to random errors, is addressed. It is assumed that the model of the spectrometric data has the form of an integral, convolution-type equation of the first kind. The method for improving the resolution consists of numerically solving this equation on the basis of the acquired data. A new algorithm for dealing with this problem is proposed; it is based on the Kalman filter constrained in such a way that the negative values of the solution are suppressed. The efficiency of this constrained algorithm is demonstrated using both synthetic and real-world data.< >
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ISSN:0018-9456
DOI:10.1109/19.368111