节点文献
时变系统全局最优加权观测融合白噪声反卷积平滑器
Globally Optimal Weighted Measurement Fusion White Noise Deconvolution Smoother for Time-varying Systems
【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。
【Abstract】 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.
【关键词】 时变系统;
加权观测融合;
白噪声估值器;
反卷积;
全局最优性;
反射地震学;
Kalman滤波方法;
【Key words】 time-varying system weighted measurement fusion white noise estimator deconvolution global optimality reflection seismology Kalman filtering method;
【Key words】 time-varying system weighted measurement fusion white noise estimator deconvolution global optimality reflection seismology Kalman filtering method;
【基金】 国家自然科学基金(60374026);黑龙江大学自动控制重点实验室基金资助
- 【文献出处】 科学技术与工程 ,Science Technology and Engineering , 编辑部邮箱 ,2006年05期
- 【分类号】TN911
- 【下载频次】97