群目标跟踪自适应IMM算法

Adaptive IMM tracking algorithm of group targets

摘要:

为提高对机动群目标在高量测误差下的跟踪性能，提出了一种自适应IMM群目标跟踪算法.首先，在群质心状态估计中，引入带有多重次优渐消因子的强跟踪滤波算法，提高机动阶段时对群质心状态估计的精度.其次，在扩展状态估计中，考虑量测精度对于扩展状态的影响，将量测误差和扩展状态同时纳入到量测似然函数的构建中，应用新息计算和渐消记忆迭代过程自适应更新量测误差协方差矩阵.最后，通过quasi-Bayesian方法自适应更新模型转换概率，利用量测数据修正模型转换概率，抑制非匹配模型作用，放大匹配模型作用，实时匹配跟踪模型与目标运动状态.仿真实验结果表明，该方法有效提高了对群质心状态和扩展状态的估计精度.

Abstract:

This paper proposes an adaptive interactive multiple models (IMM) tracking algorithm in order to improve tracking performance of strong maneuvering group targets in high measurement error. First of all, we introduce a strong tracking filtering algorithm with multiple suboptimal fading factors to improve the estimation accuracy of the group centroid in the strong maneuvering stage. Secondly, considering the influence of measurement accuracy on the extension state, the measurement error and extension state are formulated in a likelihood function, and then the error covariance of the measurement can be adaptively updated by using the innovation and the memory fading iterative process. Finally, we use the quasi-Bayesian approach to adaptively update the model transition probability. The model transition probability is modified by the measurement to suppress the non-matching model and amplify the matching model. By this way, the tracking model and targets state can be matched in real time. The simulation results show that, the proposed method is effective to improve the estimation accuracy of the centroid state and the extension state.