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[The study of baseline estimated in digital XRF analyzer]
Min Wang1, Jian-Bin Zhou, Fang Fang
1The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China. wangmin929@163.com
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|April 17, 2013
Summary
A new Double-Forgotten adaptive Kalman filter improves baseline estimation in digital X-ray fluorescence, enhancing energy resolution by overcoming filtering divergence and slow convergence.
Area of Science:
- Physics
- Instrumentation
- Signal Processing
Context:
- Digital X-ray fluorescence (XRF) analyzers are crucial for elemental analysis.
- Instrument performance, specifically energy resolution, is negatively impacted by unstable baseline voltage.
- Existing Kalman filter algorithms (classic, Sage-Husa, improved Sage-Husa) show inadequate baseline filtering effects.
Purpose:
- To address the poor baseline filtering performance of existing Kalman filter algorithms in digital XRF.
- To develop an improved adaptive Kalman filter algorithm for accurate pulse signal baseline estimation.
- To enhance the energy resolution of digital XRF instruments.
Summary:
- A novel Double-Forgotten adaptive Kalman filter algorithm, based on the Sage-Husa model, was developed for digital XRF pulse signal baseline estimation.
- This new algorithm effectively solves filtering divergence and avoids slow baseline convergence issues.
- Experimental results demonstrate a significant improvement in baseline filtering and pulse baseline restoration.
Impact:
- The optimized algorithm significantly improves the energy resolution of digital X-ray fluorescence instruments.
- Enhanced baseline stability leads to more accurate and reliable elemental analysis.
- This advancement contributes to the development of more precise and efficient XRF technology.
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