摘要
针对机动目标跟踪中固定延迟平滑估计算法的精度问题,当具有一般相关过程噪声和量测噪声时,提出了离散线性系统最优固定延迟平滑估计算法.该算法通过将延迟区间内全部量测进行集中式扩维,并对误差传递进行分析,从而精确地给出了误差间的相关性.在线性无偏最小方差意义下对系统状态进行递推估计,新算法在噪声的高斯分布假设下是最优的.仿真实验结果表明了该算法的有效性.
In view of estimation performance for maneuvering target tracking, an optimal fixed-lag smoothing algorithm is proposed for discrete-time linear system with general correlated measurement and process noises. The correlations between the errors are calculated precisely by the analysis of the error transfer property. Based on the linear unbiased minimum variance estimation theory, the algorithm estimates the system states recursively using centralized expanding-dimension method with all measurements in the fixed lag interval. The algorithm is optimal under the hypothesis of Gauss distribution based on the best linear unbiased estimation （BLUEt theory. Simulation results show the effectiveness of the proposed algorithm.
出处
《控制与决策》
EI
CSCD
北大核心
2009年第3期455-458,共4页
Control and Decision
基金
国家自然科学基金项目(60502021)
关键词
目标跟踪
相关噪声
定延迟平滑
Target tracking
Correlated noises
Fixed-lag smoothing
