通过传感器网络实现分布式联合传感器注册与多目标跟踪
Distributed Joint Sensor Registration and Multitarget Tracking Via Sensor Network
AI总结：
本文提出了一种基于传感器网络的分布式联合传感器注册与多目标跟踪方法，通过共识卡数概率假设密度滤波器递归计算每个节点的后验RFS密度，并利用平均Kullback-Leibler散度最小化来优化传感器注册参数。
摘要：
本文针对多目标跟踪中的分布式传感器网络注册问题，每个传感器在局部坐标系中获取目标位置的测量数据，不掌握其相邻节点的相对位置（称为漂移参数）和方位角（称为方向参数）。多目标集被建模为独立同分布的聚类有限集（RFS），并在网络中运行共识卡数概率假设密度（CPHD）滤波器，递归计算每个节点的后验RFS密度。然后，通过表达本地后验之间平均Kullback-Leibler散度差异的合适代价函数，对漂移和方向参数进行优化，以实现传感器注册。最后，通过树形网络和环形网络以及线性和非线性传感器的仿真实验验证了所提方法的有效性。
英文摘要：
This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed (i.i.d.) cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then a suitable cost function, xpressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
