基于线性无偏最小方差估计理论,提出了一种任意相关噪声异类传感器非线性系统状态矢量融合算法。
Based on linear unbiased minimum variance estimation theory, a fusion algorithm which fused the state vector of nonlinear systems with dissimilar sensors with arbitrary correlated noises is developed.
提出一种新的标量加权多传感器线性最小方差意义下的最优信息融合准则。
A new multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense.
给出一种基于噪声方差估计的稳健融合算法。
A robust fusion algorithm based on noise variance estimation is presented.
对于海面图像,分别采用感兴趣舰船目标区域的方差值、目标和背景亮度对比度这两个特征对目标进行融合识别。
As to sea image, the variance feature of region of interest and the luminance contrast feature between target and background are used to fusion recognition.
最终给出了一种基于测量方差自适应的多传感器数据融合算法。
Finally, a new algorithm of multi-sensors fusion based on the variance of the measured error adaptive is given.
使用由方差和平均梯度构造的新的评价因子——小波邻域信息量作为融合规则选取小波高频系数。
For the image high-frequency part, use the new evaluation factor - wavelet neighborhood information to choose the ultimate wavelet high-frequency coefficients.
基于线性无偏最小方差估计理论,提出了一种任意相关噪声异类传感器非线性系统状态矢量融合算法。
Based on the linear unbiased minimum variance estimation theory, an asynchronous fusion algorithm that fused the state vector of linear system with arbitrary correlated noises is developed.
融合预报协方差矩阵的自适应调整和状态空间鲁棒性估计,给出辨识戴维南等值参数的新算法。
Based on the robust estimation in the state-space and the adaptive modification of prediction covariance matrix, a new algorithm was proposed to track Thevenin equivalent parameters.
提出了一种新的标量加权线性最小方差意义下的多传感器最优信息融合算法。
A new multi-sensor optimal information fusion algorithm weighted by scalars is presented in the linear minimum variance sense.
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.
针对异步融合中心计算量大、实时性差的问题,基于估计协方差控制理论提出了一种多传感器异步数据融合算法。
In view of the heavier calculation burden and worse real-time of asynchronous fusion center, a multi-sensor asynchronous data fusion algorithm based on estimate covariance control was proposed.
在这类特殊的多传感器系统中,本文通过矩阵运算消除相关估计方差,得到了最优分布式融合估计算法。
For this special multisensor system, distributed optimal fusion algorithm is received by avoiding computing correlated estimation covariance based on the matrix operation.
该方法分析了局部影像统计特性,应用均值或均值—方差匹配正态函数,对要融合的两幅影像局部直方图进行匹配。
The proposed method analyses local image statistics and then matches the local histograms of two images to be fused by applying mean or mean variance matching normalization functions.
该算法采用协方差匹配技术,依据滤波新息,动态调整测量噪声方差,使融合系统的均方误差始终最小。
With covariance matching technique and innovation information of filtering, the noise variance is dynamically adjusted and the mean square error of the fusion system always keeps minimum.
研究了有关分批估计、自适应加权和方差估计算法在多传感器数据融合中的有效性、准确度和实时性。
The validity, accuracy and actual time of the algorithm for batched-estimation, self-adaptive weighting and variance-estimation are studied in multi-sensors data fusion.
本文对基于互协方差的航迹融合算法进行了仿真分析,并对航迹融合模型的稳定性进行了探讨。
The simulation and analysis on algorithm of multisensor track-to-track fusion is based on cross-variance functions, and analysis of the stability to model is presented.
该方法依据极大似然原理将来自不同母体(均值相同、方差不同)的随机样本有效融合,得到新的母体均值估计量。
According to maximum likelihood theory, it fuses random samples coming from different matrix (same mean different variance) in an effective way, and gains a nwe estimator of matrix mean.
对于低频融合,采用了基于领域像素相关和基于区域方差相结合的融合策略。
For low frequency fusion, it is used to combine with bases on domain pixel correlation and regional variance.
目的通过估计误差方差阵,对多传感器组合导航系统中不同的融合数据进行定位精度比较,为系统定位提供选择数据的依据。
Aim Using the variance matrix of estimated error, positional accuracy can be compared with different mixing together data, it has put forward a kind of basis for system location to select data.
提出了结合遥感影像的局部相关矩和局部方差进行融合的方法。
A fusion method is proposed to merge remote sensing images using rules combining correlation moment and deviation.
针对退化图像空间分辨率较低的问题,提出了一种基于归一化方差的多分辨率图像融合方法。
A major problem in using degenerative images is the low spatial resolution. In order to overcome that problem, an image fusion method based on the normalized square deviation is proposed.
针对退化图像空间分辨率较低的问题,提出了一种基于归一化方差的多分辨率图像融合方法。
A major problem in using degenerative images is the low spatial resolution. In order to overcome that problem, an image fusion method based on the normalized square deviation is proposed.
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