X荧光分析仪中数字基线估计的研究

在数字化X荧光分析仪中，不稳定的基线电压，会直接影响到仪器的性能，造成能量分辨率下降。基于此，利用卡尔曼滤波算法，对数字化后的X射线荧光信号进行基线估计。由于现有的经典卡尔曼滤波、简化sage-husa和改进sage-husa算法模型进行基线滤波的效果都不佳，因此有必要对现有的算法进行改进和优化。提出双重遗忘法，建立新型的基于sage-husa自适应卡尔曼滤波算法模型。实验结果表明，利用该数学模型进行基线滤波，取得了很好的滤波效果。避免了滤波发散和基线收敛缓慢的问题，实现了脉冲基线恢复，提高了仪器的能量分辨率。

The Study of Baseline Estimated in Digital XRF Analyzer

For the digital X-ray fluorescence analyzer, the voltage of the instability baseline will directly affect the performance of the instrument, resulting in decreased energy resolution. In order to solve this problem, Kalman filtering algorithm was used for pulse signal baseline estimate in the digital X-ray fluorescence. Whether using the classic Kalman filter, or the simplified sage-husa, or the improved sage-husa, their baseline filtering effects were all poor. So, it is necessary to improve and optimize existing algorithms. The method of Double-Forgotten was put forward to establish a new model of adaptive Kalman filter algorithm based on the sage-husa. The experiment results show that a very good filtering effect was obtained using the mathematical model of the baseline filter. The algorithm solved the problem of filtering divergence, avoided slow convergence of baseline and realized the pulse baseline restoration, and improved the instrumental energy resolution.