基于STCKF-CPHD算法的多RAM类目标跟踪

Multi-RAM target tracking based on STCKF-CPHD algorithm

针对来袭RAM类目标机动能力强、数目多变且受到密集杂波干扰从而导致传统算法跟踪精度下降的问题,提出一种基于势概率假设密度框架下的强跟踪容积卡尔曼滤波算法(STCKF-CPHD)。首先,建立RAM类目标动力学模型,通过一阶马尔可夫过程对目标外弹道质阻比参数进行建模,得到扩维后滤波器的状态空间模型。然后,引入强跟踪技术,设计带时变渐消因子的STCKF滤波器,解决目标机动导致的模型失配问题。最后,在CPHD的框架下,对目标的质阻比、状态、数量进行联合估计。仿真结果表明,所提算法可以对来袭多RAM类目标进行有效跟踪,目标最优子模型分配(OSPA)距离的跟踪精度相较于STCKF-PHD算法提高了15%。

Aiming at the problem that the tracking accuracy of traditional algorithms is reduced due to the strong maneuverability,variable number and dense clutter interference of incoming RAM targets,a strong tracking cubature Kalman filter algorithm based on cardinalized probability hypothesis Density(STCKF-CPHD)is proposed.Firstly,the dynamic model of RAM target is established,and the exterior ballistic mass resistance ratio parameters of the targets are modeled through the first-order Markov process,and the state space model of the expanded filter is obtained.Then,strong tracking technology is introduced to design STCKF filter with time-varying fading factor to solve the model mismatch problem caused by target maneuver.Finally,under the framework of CPHD,the mass resistance ratio,state and quantity of the targets are jointly estimated.Simulation results show that the proposed algorithm can effectively track incoming multi-RAM targets,and the tracking accuracy of optimal sub-patten assignment(OSPA)distance is 15%higher than that of STCKF-CPHD algorithm.