多传感器观测融合单通道白噪声反卷积滤波器
|
摘要
利用现代时间序列分析方法,基于ARMA新息模型应用加权观测融合方法,提出了多传感器加权观测融合白噪声反卷积Wiener滤波器。同集中式和分布式融合方法相比,不仅可得到全局最优白噪声融合估值器,而且可显著地减小计算负担,便于实时应用。一个两传感器Bernoulli-Gaussian白噪声加权观测融合估值器的仿真例子说明其有效性。
By using the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, the multisensor weighted measurement fusion white noise deconvolution Wiener filter is pressed by using the weighted measurement fusion method. Compared with centralized and decentralized fusion methods, not only it give the globally optimal white noise fusion estimator, but also it can obviously reduce the computational burden, so that it is suitable for real time applications. A simulation example of weighted measurement fusion estimator for a Bernoulli-Gaussian white noise with two-sensor shows its effectiveness.
By using the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, the multisensor weighted measurement fusion white noise deconvolution Wiener filter is pressed by using the weighted measurement fusion method. Compared with centralized and decentralized fusion methods, not only it give the globally optimal white noise fusion estimator, but also it can obviously reduce the computational burden, so that it is suitable for real time applications. A simulation example of weighted measurement fusion estimator for a Bernoulli-Gaussian white noise with two-sensor shows its effectiveness.
引文
1Mendel J M.White-noise estimators for seismic data pro-cessing in oil exploration.IEEE Trans Automatic Cont-rol,1977;AC-22(5):694—706
2Mendel J M.Optimal seismic deconvolution:an estimation-based ap-proach.New York:Academic Press,1983
3Gan Q,Harris C J.Comparison of two measurement fusion methods for kalman-filter-based multisensor data fusion.IEEE Trans Aerospace and Electronic Systems,2001;37(1):273—279
4邓自立.卡尔曼滤波与维纳滤波——现代时间序列分析方法.哈尔滨:哈尔滨工业大学,2001
5邓自立.自校正滤波理论及其应用——现代时间序列分析方法.哈尔滨:哈尔滨工业大学,2003
6Sun Shuli,Deng Zili.Multisensor optimal information fusion Kalman filter.Automatica,2004;40:1017—1023
7邓自立,高媛,李云,等.基于Kalman滤波信息融合白噪声最优反卷积滤波器.科学技术与工程,2004;4(3):169—171
8崔崇信,邓自立.多传感器全局最优加权观测融合Wiener信号滤波器.科学技术与工程,2006;6(2):112—115
2Mendel J M.Optimal seismic deconvolution:an estimation-based ap-proach.New York:Academic Press,1983
3Gan Q,Harris C J.Comparison of two measurement fusion methods for kalman-filter-based multisensor data fusion.IEEE Trans Aerospace and Electronic Systems,2001;37(1):273—279
4邓自立.卡尔曼滤波与维纳滤波——现代时间序列分析方法.哈尔滨:哈尔滨工业大学,2001
5邓自立.自校正滤波理论及其应用——现代时间序列分析方法.哈尔滨:哈尔滨工业大学,2003
6Sun Shuli,Deng Zili.Multisensor optimal information fusion Kalman filter.Automatica,2004;40:1017—1023
7邓自立,高媛,李云,等.基于Kalman滤波信息融合白噪声最优反卷积滤波器.科学技术与工程,2004;4(3):169—171
8崔崇信,邓自立.多传感器全局最优加权观测融合Wiener信号滤波器.科学技术与工程,2006;6(2):112—115
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心