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针对扩展Kalman滤波器(EKF)在进行非线性估计时一致性较差的问题,提出了适于一类高阶非线性系统的最小迹扩展集员估计算法(LTESMF).该算法通过引入反馈机制实现观测更新,避免了椭球相交计算.算法用估计误差定界椭球参数矩阵的迹作为优化目标,迭代优化反馈系数.本文还提出用随机状态边界度量的收敛性来评价随机系统稳定性.并用该方法证明了LTESMF的估计误差能收敛到有界区域内.最终仿真结果表明,LTESMF的估计结果的稳态精度接近EKF,计算算效率与EKF相当,估计结果的一致性和收敛速度明显高于EKF.
Aiming at the poor consistency of Extended Kalman Filter (EKF) in nonlinear estimation, a minimum trace extension set-based estimation algorithm (LTESMF) for a class of high-order nonlinear systems is proposed. By introducing a feedback mechanism Which avoids the ellipsoid intersection calculation.The algorithm estimates the trajectory of the ellipsoid parameter matrix as the optimization objective and iteratively optimizes the feedback coefficient.This paper also proposes to evaluate the stochastic stability by using the convergence of the stochastic state boundary measure. And the results show that the estimation error of LTESMF can converge to the bounded region.The simulation results show that the accuracy of LTESMF estimation results is close to EKF, the computational efficiency is equivalent to that of EKF, and the consistency and convergence rate of the estimation results are obviously high At EKF.