基于Rényi信息增量的机动目标协同跟踪算法

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。

A Maneuvering Target Collaboration Tracking Algorithm Based on Rényi Information Gain

To solve sensor management problem when tracking one maneuvering target using multiple netted sensors,the maneuvering target collaboration tracking algorithm based on Rényi information gain is proposed. A variable structure multiple model algorithm combining current statistics model and interacting multiple model unscented kalman filter is first proposed to estimate the states of maneuvering target. One sensor is then selected according to maximal Rényi information gain to perform target tracking. Grid partition is finally performed by estimation of the optimal acceleration to update possible model sets of the target. The performance of the proposed algorithm is analyzed in general and strong maneuvering scenarios,and simulation results demonstrate that the proposed algorithm can select the optimal sensor reasonably and improves the accuracy for maneuvering target tracking.