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为了提高融合算法的精度,将UKF(Unscented Kalman Filter)算法与多传感器顺序滤波融合跟踪算法相结合,提出了基于UKF的多传感器序贯融合算法。UKF算法利用非线性方程自身的传播,估计系统状态,避免了对非线性方程线性化的过程。顺序滤波融合算法用同一时刻的量测依次更新状态,计算复杂性低。仿真结果表明,UKF顺序滤波融合跟踪算法比传统的扩展卡尔曼滤波(EKF)算法有更高的跟踪性能,是一种有效的非线性融合算法。
In order to improve the accuracy of the fusion algorithm, the unscented Kalman Filter (UKF) algorithm and multisensor sequential filtering fusion tracking algorithm are combined to propose a multi-sensor sequential fusion algorithm based on UKF. The UKF algorithm makes use of the propagation of the nonlinear equations to estimate the state of the system and avoids the process of linearizing the nonlinear equations. Sequential filtering fusion algorithm using the same moment of measurement in order to update the state, low computational complexity. Simulation results show that the UKF sequential filtering fusion tracking algorithm has higher tracking performance than the traditional EKF algorithm and is an effective non-linear fusion algorithm.