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Multi-sensor optimal information fusion Kalman filters with applications

机译：多传感器最优信息融合卡尔曼滤波器及其应用

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摘要

Using Kalman filtering theory, a new multi-sensor optimal information fusion algorithm weighted by matrices is presented in the linear minimum variance sense, which is equivalent to the maximum likelihood fusion algorithm under the assumption of normal distributions. The algorithm considers the correlation among local estimation errors, and it involves the inverse of certain matrix with high dimension. Another two new multi-sensor suboptimal information fusion algorithms weighted by vectors and weighted by scalars are given for reducing the computational burden and increasing the real-time property. Based on these fusion algorithms, the multi-sensor optimal and suboptimal information fusion Kalman filters with two-layer fusion structures are given. The simulation researches of the comparisons among them as well as the centralized filter in a radar tracking system with three sensors show their effectiveness.
机译：利用卡尔曼滤波理论，在线性最小方差意义上提出了一种新的矩阵加权的多传感器最优信息融合算法，该算法等效于正态分布假设下的最大似然融合算法。该算法考虑了局部估计误差之间的相关性，涉及到高维矩阵的逆。给出了另外两种新的向量加权和标量加权的多传感器次优信息融合算法，以减少计算量，提高实时性。基于这些融合算法，给出了具有两层融合结构的多传感器最优和次优信息融合卡尔曼滤波器。对它们之间的比较以及带有三个传感器的雷达跟踪系统中的集中滤波器的仿真研究表明了它们的有效性。

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