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首页> 外文会议>International Conference on Sensors, Measurement and Intelligent Materials >Covariance Intersection Fusion Kalman Estimator for Multi-sensor System with Measurements Delays
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Covariance Intersection Fusion Kalman Estimator for Multi-sensor System with Measurements Delays

机译：测量延迟多传感器系统的协方差交叉融合卡尔曼估计

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摘要

To handle the state estimation fusion problem between local estimation errors for the system with unknown cross-covariances and to avoid a large computation complexity of cross-covariances, for a multi-sensor linear discrete time-invariant stochastic system with time-delayed measurements, by the measurement transformation method, an equivalent system without measurement delays is obtained, and then using the covariance intersection (CI) fusion method, the covariance intersection fusion steady-state Kalman estimator is presented. It is proved that its accuracy is higher than that of each local estimator, and is lower than that of optimal Kalman fuser weighted by matrices with known cross-covariances. A Monte-Carlo simulation example shows the above accuracy relations, hence it has good performances.
机译：在具有未知交叉协方差的系统之间处理局部估计误差之间的状态估计融合问题，避免交叉协方差的大型计算复杂性，对于具有时间延迟测量的多传感器线性离散时间 - 不变随机随机系统，通过测量变换方法，获得了没有测量延迟的等效系统，然后使用协方差交叉（CI）融合方法，提出了协方差交叉融合稳态卡尔曼估计器。证明其精度高于每个本地估计器的精度，低于具有已知交叉协方差的矩阵加权的最佳卡尔曼定影器。蒙特卡罗仿真示例显示了上述准确性关系，因此它具有良好的性能。

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