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针对基于多传感器组网进行机动目标跟踪的传感器管理问题,提出了一种基于Rényi信息增量的机动目标协同跟踪算法。首先结合“当前”统计模型和交互式多模型不敏卡尔曼滤波算法设计了一种变结构多模型算法,来进行机动目标的状态估计;然后以Rényi信息增量为评价准则,选择使Rényi信息增量最大的单个传感器进行目标跟踪;最后利用得到的最优加速度估计进行网格划分,更新变结构多模型中的模型集合。在一般机动及强机动场景下进行了算法性能分析,仿真结果表明,该算法能够合理地选择传感器,提高了对机动目标的跟踪精度。
Aiming at the problem of sensor management based on multi-sensor network for maneuvering target tracking, a maneuvering target collaborative tracking algorithm based on Rényi information increment is proposed. Firstly, a variable structure multi-model algorithm is designed based on the “current” statistical model and the interactive multi-model unscented Kalman filter algorithm to estimate the state of the maneuvering target. Then, based on the Rényi information increment as the evaluation criterion, Rényi’s single sensor with the largest increment of information is used to track the target. Finally, the optimal acceleration estimation is used to mesh the model and update the model set in the variable structure multi-model. The performance of the algorithm is analyzed under the condition of normal maneuvering and strong maneuvering. The simulation results show that the algorithm can select sensors reasonably and improve the tracking accuracy of maneuvering targets.