分布式传感器网络中基于条件后验克拉美-罗下界的被动声目标跟踪局部节点选择算法（英文）

CPCRLB based local node selection for passive acoustic target tracking in a distributed sensor network

针对分布式传感器网络下的被动声目标跟踪问题,提出了一种基于条件后验克拉美罗下界(Conditional Posterior Cramér-Rao Lower Bounds, CPCRLB)的局部传感器节点选择算法,基于被动声探测背景下的纯方位量测数据,采用粒子滤波器推导得到了CPCRLB的近似解析表达式,进而在该CPCRLB的基础上定义了节点效用贡献作为节点选择准则,结合分布式传感器网络的特点提出了一种局部节点选择方法,节点无需知道全网传感器节点的信息,而是仅利用局部节点信息来决定下一时刻节点的活动状态,从而在实现自治节点选择的同时大大减少网络通信量。通过仿真结果表明,该算法在跟踪精度、能量消耗和计算复杂度方面都表现出较好的性能。

For passive acoustic target tracking in a distributed sensor network, this paper proposed a local node selection algorithm based on conditional posterior Cramer-Rao lower bounds （CPCRLB）. The approximate analytical expression of CPCRLB is derived by utilizing particle filters for bearing-only measurements, and it is used to define the utility contribution as the node selection criterion. In the proposed algorithm, each node can only use the local information to determine whether to be activated without the knowledge of all nodes in the network. Simulation results prove the effectiveness of our method and show good performance in tracking accuracy, energy consumption and computational complexity.