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为了进一步提高群目标交互多模型跟踪算法的估计性能,提出一种改进的群跟踪算法.首先,通过采用模型转换概率的自适应算法,优化模型与目标运动模式的实时匹配.并通过引入强跟踪滤波(STF,Strong Tracking Filter)中的渐消因子,提高机动阶段时的群质心的状态估计精度.其次,分别利用概率加权法和标量加权法完成群质心状态和扩展状态的融合估计.最后在变分贝叶斯滤波的基础上,建立完整的跟踪算法流程.仿真实验结果表明,该方法不仅能够提高群质心状态和扩展状态的估计精度,还能有效降低机动阶段时的峰值误差.
In order to further improve the estimation performance of group-target interactive multi-model tracking algorithm, an improved group tracking algorithm is proposed.Firstly, an adaptive algorithm of model transition probability is used to optimize the real-time matching of model and target model.And by introducing strong tracking Fading factors in Strong Tracking Filter (STF) to improve the state estimation accuracy of the mass-centered heart in the maneuver stage.Secondly, the fusion estimation of the mass-centered state and the extended state is completed by using the probability weighting method and the scalar weighting method respectively.Finally, Based on the variable Bayesian filtering, a complete tracking algorithm is established. The simulation results show that this method can not only improve the estimation accuracy of the mass state and the extended state, but also effectively reduce the peak error in the maneuver stage.