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.
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。
Proposes to measure the wrist force using the data fusion method according to output variance of finger force sensors on the gripper.
提出根据手爪上的多个指力传感器的输出变化，通过数据融合方法得到腕力的数值。
A new multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense.
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 robust fusion algorithm based on noise variance estimation is presented.
给出一种基于噪声方差估计的稳健融合算法。
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.
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。
Finally, a new algorithm of multi-sensors fusion based on the variance of the measured error adaptive is given.
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。
For low frequency fusion, it is used to combine with bases on domain pixel correlation and regional variance.
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。
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.
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。
A new multi-sensor optimal information fusion algorithm weighted by scalars is presented in the linear minimum variance sense.
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。
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.
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。
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.
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 validity, accuracy and actual time of the algorithm for batched-estimation, self-adaptive weighting and variance-estimation are studied in multi-sensors data fusion.
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.
本文应用了一种基于自适应加权数据融合的灰色优势分析方法对多传感器数据进行处理。
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.
本文应用了一种基于自适应加权数据融合的灰色优势分析方法对多传感器数据进行处理。
应用推荐