两传感器自校正信息融合白噪声Wiener反卷积滤波器
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摘要
应用现代时间序列分析方法,基于自回归滑动平均(ARMA)新息模型,对于带未知模型参数和噪声方差的两传感器反卷积系统,提出了自校正信息融合白噪声Wiener反卷积滤波器。它具有渐近最优性。一个Bernoulli-Gaussian白噪声反卷积的仿真例子说明了其有效性。
By the modern time series analysis method, based on the on-line identification of the autoregressive moving aver-age (ARMA) innovation model, a self-tuning information fusion white noise Wiener deconvolution filter is presented for two-sensor deconvolution systems with unknown model parameters and unknown noise variances. It has asymptotic optimality. Asimulation example for Bemoulli-Gaussian white noise deconvolution shows its effectiveness.
By the modern time series analysis method, based on the on-line identification of the autoregressive moving aver-age (ARMA) innovation model, a self-tuning information fusion white noise Wiener deconvolution filter is presented for two-sensor deconvolution systems with unknown model parameters and unknown noise variances. It has asymptotic optimality. Asimulation example for Bemoulli-Gaussian white noise deconvolution shows its effectiveness.
引文
1 邓自立,高媛,马建为.两传感器信息融合最优自噪声反卷积Wiener滤波器.科学技术与工程,2003;3(3) :216-218
2 Mendel J M.Optimal seismie deconvolution: an estimation-based approach. New York: Aeademid Press,1983
3 邓自立.卡尔曼滤与维纳滤波--现代时间序列分析方法.哈尔滨:哈尔滨工业大学出版社,2001
4 邓自立,马建国,杜洪越.ARMA模型参数估计的两段最小二乘法,科学技术与工程.2002． 2(5) :3-5
2 Mendel J M.Optimal seismie deconvolution: an estimation-based approach. New York: Aeademid Press,1983
3 邓自立.卡尔曼滤与维纳滤波--现代时间序列分析方法.哈尔滨:哈尔滨工业大学出版社,2001
4 邓自立,马建国,杜洪越.ARMA模型参数估计的两段最小二乘法,科学技术与工程.2002． 2(5) :3-5
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