混沌序列具有容易生成、对初始条件敏感以及具有白噪声的统计特性。
The chaotic sequence has easy to produce, is sensitive to the initial condition as well as has the statistical property of the white noise.
众所周知，卡尔曼滤波的成功应用需要事先准确知道观测噪声的统计特性。
It is well known that the successful applications of the Kalman filter are dependent on whether the prior knowledge of the statistical characteristics of the measurement noise is known.
该方法利用混沌序列具有容易生成、对初始条件敏感，以及具有白噪声的统计特性等特点。
This method makes use of good properties of chaotic sequences, such as ease of generation, sensitive dependence on their initial condition and noise like statistic characteristics.
它能够在线估计虚拟系统噪声的统计特性，从而消除了动态模型线性化误差带来的不良影响。
Owing to estimate the statistics of virtual noise on line, it overcomes the bad affect caused by linearization of nonlinear dynamic model.
它能够在线估计虚拟观测噪声的统计特性，从而克服了观测模型线性化误差带来的不良影响。
Owing to estimating the statistics of virtual noise on line, it overcomes the bad affect caused by linearization of nonlinear observation model.
根据设备系统噪声及量测噪声的统计特性，对采样信号运用卡尔曼滤波技术，提高了测量精度。
By measuring equipments' noise and estimating its value through Kalman filter, the accuracy of the equipments was effectively enhanced.
该算法将噪声的统计特性引入到投影迭代的限制条件之中，使得该推广的POCS算法具有很好的去噪声能力。
The generalized POCS algorithm introduces the statistics of the noise to the constraints of projection iteration so that the algorithm is able to eliminate the noise effectively.
该文利用小波包变换的时频局部分析能力，研究了非高斯分布平稳随机噪声的统计特性，揭示了非高斯噪声信号的信号结构。
By exploiting wavelet packet transform to analyze signals both in time and frequency space, this paper researches the statistic property of non-Gaussian stationary noise and its signal structure.
由于系统方程是时变的，在测量过程中，系统噪声和观测噪声的统计特性等很难精确地估计或测定，事实上很多误差模型都不能简单的设为白噪声。
Because the system equation is changed with time, during the measurement, the statistic character of the system noise and the observation noise cannot be estimated or be determined accurately.
基于图像在小波域的马尔可夫随机场模型(MRF)结构，结合SAR图像中相干斑噪声的统计特性，本文提出了一种新的小波域相干斑抑制方法。
Integrating the statistical characteristics of speckle noise in SAR images with wavelet-domain Markov random field (MRF) structure of images, a new wavelet-domain spec.
测程法噪声统计特性的自适应估计问题是机器人定位研究的难点。
Adaptive estimation of the statistic character of odometric noises is a difficult problem for localization.
与已有的融合方法相比，文章提出的神经数据融合方法具有非偏倚的统计特性而且不需要关于噪声协方差的任何先验知识。
Compared with the existing fusion method, the proposed neural data fusion method has an unbiased statistical property and does not require any prior knowledge about the noise covariance.
应用自适应滤波解决了实际中噪声统计特性不准确或未知的问题，提高了导航精度。
The adaptive filter is applied to the system in order to solve the question of inaccuracy or unknown noise statistics and to make navigation accuracy increase.
研究了信号及杂波、噪声在信号处理之后的统计特性，并通过仿真系统产生了目标回波数据及检测背景噪声。
This paper also studies the statistics characteristic of signal, clutter and noise after the system disposal, and generates target data and detecting background noise using the simulated system.
该算法实现了横摆角速度的线性最小均方误差估计，且可对汽车行驶过程中的系统噪声和观测噪声统计特性进行在线估计。
This algorithm can realize linear minimum mean square error estimation of yaw rate, and on-line estimate statistical characteristic of system noise and observation noise during vehicle running.
针对滤波发散的问题，引入了一种在线估计观测噪声统计特性的自适应滤波方法。
To solve the problem of filtering divergence, a method to estimate the statistical feature of measurement noise was introduced.
因此，利用简单混沌数学模型迭代产生的信号具有随机性，其统计特性类似白噪声。
So the signal generated by iteration of simple chaos mathematical model get the feature of randomness and owns the statistical characteristics similar to white noise.
导致发散的原因很多，其中很重要的原因就是缺乏对系统噪声和测量噪声统计特性的了解。
There are many reasons to cause divergence. One of the most important reasons is due to the unknown statistical properties of system noise and measurement noise.
本文将小波包变换用于非高斯噪声统计特性的研究，提出一种新的非高斯分布噪声下的信号检测算法。
In the dissertation, it puts wavelet-packet decomposition into the study on non-gaussian noise. It offers a new signal dection method under non-gaussian noise backgroud.
该方法根据噪声时域特性将其分为结构性噪声和非结构性噪声两类，它们对统计检验功效造成的影响在文中分别进行了讨论。
The fMRI noise is classified into structured and unstructured noise by their temporal characteristics, and their impacts to efficiency of statistical tests are discussed separately.
考虑到输出信号噪声成分的统计特性，得到恒星光子计数观测的计算机模拟方法。
The output signals of the stellar meridian observation using a photon counter are obtained by means of combining the computed signals wiry recording noises.
讨论了复杂信号系统中处理噪声信号的方法，提出了一种基于自适应非线性变换器的抑噪系统的设计方法，该系统考虑了干扰作用的统计特性。
Simulation results show effected by complex-labile interfere the noise suppression system with the adaptive nonlinear changer may guaranteed noise suppression performance of complex signal system.
该算法利用了图像数据的高阶统计特性对背景噪声进行盲估计，并通过相邻重叠分块间的特征估计来判断图像哪些部分被篡改。
By computing high order statistic characteristics of the background noise and estimating the neighboring overlap blocks, the algorithm could locate the forgery parts.
依据统计特性来估计噪声在空频域上的分布，并依此构造具有空频自适应性的边缘增强增益；
Then, the edge enhancement gains are evaluated according to the noise distributions on the spatial-frequency domain;
该方法通过在线联合估计噪声统计特性和系统状态，有效地解决了GFSINS/GPS采用间接法组合时，无法得到准确系统噪声统计特性的问题。
Indirect method integrated can't get noise static character exactly. This method solves above problem effectively by estimation of noise static character and system vector at real-time.
该方法通过在线联合估计噪声统计特性和系统状态，有效地解决了GFSINS/GPS采用间接法组合时，无法得到准确系统噪声统计特性的问题。
Indirect method integrated can't get noise static character exactly. This method solves above problem effectively by estimation of noise static character and system vector at real-time.
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