| 1. | Randomness in this case is modelled as a gaussian white noise process
模式中之随机性乃视为一高斯白噪音过程。 |
| 2. | The two - order volterra model can be identified with the correlation analysis and frequency - domain analysis when the input is gaussian white noise
通过相关分析、频域分析，采用高斯白噪声输入信号，可以对二阶volterra模型进行辨识。 |
| 3. | Our experiments show that this method is also robust to gaussian white noise . resampling and smoothing etc . . at last , some discussions and conclusions are given
实验结果显示，我们的算法在高斯白噪声、重采样、运动光滑等攻击下有很好的鲁棒性。 |
| 4. | In the article , a self - learning stochastic method based on the statistical pattern recognition is presented for attitude control under the disturbance of blind mixed gaussian white noises
摘要本文提出一种基于统计模式识别，针对盲混合高斯白噪声干扰下卫星姿态控制的方法。 |
| 5. | When sampling period is very low or close to ideal case for signal detection in additive gaussian white noise ( agwn ) , the detection circuit with matched filter is optimal
摘要在加性高斯白噪声信号检测中，在采样周期极小或接近理想的情况下，连有匹配滤波器的检测电路是最佳的。 |
| 6. | The statistical property of gaussian white noise in the monoscale ridgelet domain was analyzed , and a new image threshold denoising algorithm based on monoscale ridgelet transform was proposed
分析了高斯白噪声在单尺度脊波域中的统计性质，提出了一种新的基于单尺度脊波变换的阈值滤波算法。 |
| 7. | The statistical property of gaussian white noise in the monoscale ridgelet domain was analyzed , and a new image threshold denoising algorithm based on monoscale ridgelet transform was proposed
摘要分析了高斯白噪声在单尺度脊波域中的统计性质，提出了一种新的基于单尺度脊波变换的阈值滤波算法。 |
| 8. | In addition , the influence mechanism of noise on the enhancement ratio is discussed . from the simulation examples , we find the law that random gaussian white noise influencing synthesized images
另外还分析了噪声对分辨率提高倍数的影响，通过仿真实验，找到了合成图像受随机高斯白噪声影响的规律。 |
| 9. | Two - levels and three levels algorithms are discussed in the more general system , which has control input and in which the processing noise and measurement noise are correlated gaussian white noise with nonzero mean
针对存在控制输入、过程噪声与观测噪声相关且为非零均值高斯白噪声的多传感器系统，推导了二级、三级融合算法。 |
| 10. | Finally , we discuss application of kalman filtering . the optimality of multi - sensor kalman filtering fusion with feedback is presented and a filter bank based on wavelets and equipped with a miltiscale kalman filter is proposed for estimating fractal signal in additive gaussian white noise
最后，我们讨论了卡尔曼滤波在实际中的应用，分析了多传感器反馈卡尔曼滤波融合的最优性，并且基于小波变换利用卡尔曼滤波对淹没在高斯白噪声中的分形信号进行了波形估计。 |
