【摘要】 针对单目标跟踪中多传感器平滑融合算法估计精度问题,提出了具有一般相关过程噪声与量测噪声时的离散线性系统新的平滑融合估计算法.该算法通过将给定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.与不考虑相关性以及仅考虑部分相关性的卡尔曼平滑融合算法相比,新的固定区间平滑融合算法在噪声的高斯分布假设下具有明显的优越性,且其跟踪性能随噪声相关性增强而优越性明显,而固定延迟平滑融合算法是次优的.仿真实验进一步验证了本文算法在一般相关噪声环境下的优越性.更多还原

【Abstract】 In view of multi-sensor fusion estimation performance for maneuvering target tracking,a new smoothingfusion algorithm is proposed for discrete-time linear system with general correlated measurement and process noises.The correlations between the errors are calculated precisely by analysis of the error transmission property.Based on the linear unbiased minimum variance estimation theory,the new algorithm estimates the system states recursively by using centralized expanding-dimension method with all measurements in the given interval.Compared with the uncorrelated or partially correlated Kalman smoothing-fusion algorithm,the new fixed-interval smoothing-fusion algorithm is superior under the hypothesis of Gauss distribution of noise,and the fixed-lag algorithm is suboptimal.Simulation results verified the superiority of the new proposed algorithm in the general correlated noises.It was also shown that its improvement of the tracking performance increased with the increasing of the correlation coefficient.更多还原