【作者】 叶军军； 彭冬亮； 葛泉波；

【Author】 Ye Junjun,Peng Dongliang,Ge Quanbo Institute of Information and Control,Hangzhou Dianzi University,Hangzhou 310018

【机构】 杭州电子科技大学信息与控制研究所；

【摘要】 本文以连续的分布式多传感器线性动态系统为研究对象,采用采样点顺序离散化思想提出一种新的异步采样顺序融合算法.该算法首先将来自各个传感器的测量值在融合中心的坐标系中和时钟下进行映射统一;然后,选取融合周期内各采样时刻对连续状态系统进行顺序离散化,从而可获得本周期内各采样点间的状态方程及相应的测量方程;最终,可直接使用线性最小均方误差意义最优的线性Kalman滤波来实现本周期内各异步采样量测的顺序滤波融合.同现有典型的基于等价伪量测的异步融合算法相比,本文算法具有较少的计算量,较好的实时性和较高的估计融合精度,文中详细推证了融合算法的具体形式,最后通过计算机仿真证明新算法的有效性.更多还原

【Abstract】 This paper proposes a novel sequential asynchronous fusion algorithm by using the idea of sequential discretization of the sampling points based on a continuous and distributed multi-sensor linear dynamic system.Firstly,it maps and unifies all measurements in the reference frame and clock with fusion centre.Secondly,selecting every sampling time in the fusion period to discretize the continuous state system sequentially,we get the state equation and the relevant measurement equation between every sampling point in this period.Finally,using the best linear Kalman filter in the sense of LMMSE directly,the sequential filtering fusion of asynchronous sampling measurements in this period can be realized.Compared with the existing typical asynchronous algorithms which depend on equivalent pseudo-measurements,the proposed algorithm has lower computational load,better real-time and accurateness.This paper elaborates the form of this new algorithm,and finally computer simulation demonstrates validity of the new algorithms.更多还原