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中文摘要：
白噪声反卷积或输入白噪声估计问题在石油地震勘探中有重要的应用背景。对带多传感器和带不相关噪声的线性离散时变随机系统，应用Kalman滤波方法，基于加权最小二乘法，提出了全局最优加权观测融合白噪声反卷积平滑器。一个Bernoulli—Gaussian输入白噪声融合平滑器的Monte Carlo仿真例子说明了其有效性。
英文摘要：
White noise deconvolution or input white noise estimation problem has important application background in oil seismic exploration. For the linear discrete time-varying stochastic systems with multisensor and uncorrelated noises, a globally optimal weighted measurement fusion white noise deconvolution smoother is presented based on the method of weighted least squares, using Kalman fitering method. A Monte Carlo simulation example for a Bernoulli-Gaussian input white noise fusion smoother shows its effectiveness.
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