协同网络下分布式跟踪的动态传感器管理

协同网络系统采用分等级的分布式信息处理结构,在实现对杂波中多目标的精确跟踪时,需要考虑量测起源不确定性问题和传感器对目标及局部融合中心的同时分配问题.针对上述问题,文中提出了一种适用于分布式跟踪的动态传感器管理方法.首先以修正Riccati差分方程的解作为目标跟踪性能的评价标准,据此构建传感器管理的目标函数,然后根据分层优化策略,利用蚁群算法实时求近似优解,最后基于分布式融合算法获取多目标的最优跟踪轨迹.仿真结果表明,与NN分簇和MV分簇两种传感器管理方法相比,文中所提方法具有更高的目标跟踪精度和网络资源利用率.

Dynamic Sensor Management for Distributed Tracking in Collaborative Network

In the accurate tracking of multiple targets in clutter by using a collaborative network system with hierarchical and distributed information processing structure,both the measurement uncertainty and the sensor assignment to the target as well as the local fusion centers must be taken into consideration.In order to solve these problems,a dynamic sensor management method for distributed tracking is proposed.In this method,first,the criteria to evaluate the tracking performance are determined based on the solution to the modified Riccati difference equation,with which the objective function of the dynamic sensor management is constructed.Then,according to a hierarchical optimization strategy and by using the improved ant colony algorithm,an approximate optimal solution is obtained in real time.Finally,the optimal multi-target tracking trajectories are obtained by using a distributed fusion algorithm.Simulated results indicate that,as compared with the two dynamic sensor management methods,namely NN-Clustering and MV-Clustering,the proposed method is of higher multi-target tracking accuracy and higher utilization rate of network resource.