机动多目标跟踪中的传感器控制策略的研究

针对机动多目标跟踪中的传感器控制问题, 本文提出一种基于信息论的多模型多伯努利滤波器的控制方案. 首先, 基于随机有限集(Random finite set, RFS)方法给出信息论下的传感器控制的一般方法; 其次, 本文给出多模型势均衡多目标多伯努利滤波器的序贯蒙特卡罗实现形式. 此外, 提出一种目标导向的多伯努利概率密度的粒子采样方法, 并借助该方法近似多目标概率密度, 继而利用Bhattacharyya 距离求解最终的控制方案. 典型机动多目标跟踪问题的仿真应用验证了本文传感器控制方法的有效性.

Sensor Control Strategy for Maneuvering Multi-target Tracking

In consideration of the sensor control problem for maneuvering multi-target tracking, this paper proposes an information theory based control policy using multi-model multi-Bernoulli filter. First, a sensor control approach is presented in the information theory framework based on random finite set. Then, the sequential Monte Carlo implementation of multi-model cardinality balanced multi-target multi-Bernoulli filter is formulated. Moreover, this paper proposes a target-oriented multi-Bernoulli particle sampling method to approximate multi-target probability density. And the final control policy based on Bhattacharyya distance is solved through this sampling method. Finally, simulation results show the effectiveness of the proposed sensor control approach applied to a typical maneuvering multi-target tracking problem.