【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。更多还原

【Abstract】 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.更多还原