一类多速率多传感器系统的状态融合估计算法

闫莉萍; 刘宝生; 周东华; 文成林

doi:10.3724/SP.J.1146.2005.00566

一类多速率多传感器系统的状态融合估计算法

doi: 10.3724/SP.J.1146.2005.00566

闫莉萍^{①;刘宝生},
刘宝生,
周东华,
文成林

基金项目:

国家自然科学基金(60234010, 60434020)和国家973计划(2002CB312200)资助课题

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出版历程
- 收稿日期: 2005-05-19
- 修回日期: 2005-09-09
- 刊出日期: 2007-02-19

A Class of State Fusion Estimation Algorithm for Multirate Multisensor Systems

摘要

摘要: 基于不同传感器以不同采样率对同一目标状态进行观测的多传感器单模型动态系统，该文提出了一种状态融合估计算法。不同传感器之间采样率之比可以是正有理数。该算法不仅具有好的实时性，而且在线性最小方差意义下是最优的。进一步可以证明：融合多个传感器获得的最高采样率下状态的估计值优于单传感器的估计结果，而减少任何一个传感器的信息所获得的估计值的误差协方差都将增大。仿真结果验证了算法的可行性与有效性。
- 数据融合;多速率;状态估计;Kalman滤波
Abstract: Based on mulitsensor single model dynamic systems, a state fusion estimation algorithm is presented. Multisensors observe the same target, where different sensors may have different sampling rates and the ratio between them may be positive rational numbers. The algorithm is in real-time, and the optimal in the sense of linear minimum covariance. It is proved that the fused estimate is more accurate than the Kalman filtering result based on single sensors. The fused estimation error covariance will increase if any of the sensors information is neglected. The feasibility and effectiveness of the algorithm are shown through simulation results.

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