Qin Xia, Shen Lan-sun
Signal and Information Processing Lab, Beijing Polytechnic University, Beijing 100022, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2002 Dec;22(6):1009-12.
Kalman filtering is a recursive algorithm, which has been proposed as an attractive alternative to correct overlapping interferences in ICP-AES. However, the noise in ICP-AES contaminates the signal arising from the analyte and hence limits the accuracy of kalman filtering. Wavelet transform is a powerful technique in signal denoising due to its multi-resolution characteristics. In this paper, first, the effect of noise on kalman filtering is discussed. Then we apply the wavelet-transform-based soft-thresholding as the pre-processing of kalman filtering. The simulation results show that the kalman filtering based on wavelet transform can effectively reduce the noise and increase the accuracy of the analysis.
卡尔曼滤波是一种递归算法，它已被提出作为一种有吸引力的替代方法，用于校正电感耦合等离子体原子发射光谱法（ICP - AES）中的重叠干扰。然而，ICP - AES中的噪声会污染分析物产生的信号，从而限制了卡尔曼滤波的准确性。小波变换由于其多分辨率特性，是信号去噪中的一种强大技术。在本文中，首先讨论了噪声对卡尔曼滤波的影响。然后我们将基于小波变换的软阈值处理应用于卡尔曼滤波的预处理。仿真结果表明，基于小波变换的卡尔曼滤波可以有效降低噪声并提高分析的准确性。
