IMM迭代扩展卡尔曼粒子滤波跟踪算法

该文提出了一种交互式多模型(IMM)迭代扩展卡尔曼粒子滤波机动目标跟踪算法。该算法在多模型中使用了改进的粒子滤波器,通过对迭代扩展卡尔曼滤波(IEKF)的测量更新按照高斯牛顿方法进行修正,减小了非线性滤波带来的线性化误差,然后利用修正的IEKF来产生粒子滤波的重要性密度函数,使其融入最新观测信息。最后将所提算法与交互式多模型粒子滤波(IMMPF)进行了比较,仿真结果表明该算法具有更好的跟踪性能。

IMM Iterated Extended Kalman Particle Filter Based Target Tracking

A new algorithm based on Interacting Multiple Model (IMM) iterated extended Kalman particle filter is proposed for maneuvering target tracking, which uses an improved Particle Filter (PF) in multiple model. First, the Iterated Extended Kalman Filter (IEKF) is modified by providing a new measurement update based on Gauss-Newton iteration, thus the linearity error is reduced. Then the modified IEKF is used to generate the importance density function of PF, which integrates the latest observation into system state transition density. Finally, simulation results are presented to demonstrate the improved performance of the proposed method over Interacting Multiple Model Particle Filter (IMMPF).