通过对回归滤波器的计算复杂度的研究，寻找使回归滤波器计算量最小的正交多项式。
Through the study of the computational complexity of regression filter, the orthogonal polynomials which make the regression filter has the minimum computational complexity are found.
从正交多项式的拟合出发，给出了回归滤波器抑制地物杂波的基本原理及其滤波实现过程。
From the orthogonal polynomials fit, this dissertation gives the basic theory of the regression filter for ground clutter suppression and the filtering process using a regression filter.
分析了回归滤波器频率响应特性，比较了回归滤波器与IIR椭圆地物杂波滤波器的计算复杂度。
We analyze the frequency response characteristics of the regression filter and compare the computational complexity of regression filter with that of fifth-order elliptic IIR ground clutter filter.
在基于一组实际采集的雷达信号的基础上，给出了回归滤波器的地物杂波抑制比随着滤波器阶数的变化情况。
Using an actual collected radar signal, we change the orders of the regression filter from first-order to tenth-order and obtain the corresponding ground clutter suppression ratios.
在天气回波信号谱宽固定的情况下，通过改变天气回波信号的平均多普勒频率，分析了回归滤波器对它的衰减情况。
Fixing the spectrum width of the weather echo signals, we change theirs mean Doppler frequency and filter them using regression filter. After filtering, we analyze the attenuation of them.
文章讨论如何根据最小二乘原理和回归模型设计出自适应滤波器，以用于消除图像噪声。
This paper discusses how to design an adaptive filter to remove the noise of an image in terms of the principle of least squares and regression model.
根据最小二乘原理和回归模型设计出消除噪声的自适应滤波器，以消除图像的噪声。
An adaptive filter is designed in the paper to remove the noise of an image in terms of the principle of least squares and regression model.
根据模糊图象和估计的理想图象，利用多元回归分析设计滤波器，最后将滤波器应用于整幅图象完成图象的复原。
According to the degraded image and the ideal subimage, a filter is designed by using multiple regression analysis and then applied to restore the whole image.
其中内部动态元分别由带有局部激活反馈和局部输出反馈的自回归滑动平均滤波器构成。
The internal dynamic elements are auto-regressive moving average filters with local activation feedback and local output feedback, respectively.
为了提高卡尔曼滤波估计精度，提出了一种基于回归神经网络补偿卡尔曼滤波器估计误差的方法。
A method based on recurrent neural network compensation Kalman's evaluation error is proposed in order to enhance the evaluation precision.
为了提高卡尔曼滤波估计精度，提出了一种基于回归神经网络补偿卡尔曼滤波器估计误差的方法。
A method based on recurrent neural network compensation Kalman's evaluation error is proposed in order to enhance the evaluation precision.
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