基于平方根UKF的多传感器融合跟踪

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.

Multiple-Sensor Fusion Tracking Based on Square-Root Unscented Kalman Filter

In order to improve the accuracy of fusion algorithm, a new distributed fusion algorithm was proposed by combing the square-root Unscented Kalman filter with multiple-sensor sequential filter fusion tracking algorithm. The system state was estimated by UKF which propagated the state by true nonlinear equation and avoid the linearization of nonlinear equation. The measures within the same time were used to update the state sequentially by the sequential filter fusion algorithm. The simulation results show that the new algorithm has higher tracking performance than traditional extended Kalman filter fusion method. The new algorithm has lower complexity so it is a very effective nonlinear fusion algorithm.