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 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, expressing 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.

中文翻译：

---

通过传感器网络进行分布式联合传感器注册和多目标跟踪

本文讨论了用于多目标跟踪的传感器网络的分布式注册。每个传感器在局部坐标系中获取目标位置的测量值，不知道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）。多目标集被建模为一个独立同分布的集群随机有限集 (RFS)，并在网络上运行一个共识基数概率假设密度 (CPHD) 过滤器，以在每个节点递归计算后验 RFS 密度。然后，一个合适的成本函数，根据平均 Kullback-Leibler 散度表示局部后验之间的差异，相对于传感器注册目的的漂移和方向参数被最小化。这样，为联合传感器配准和多目标跟踪设计了一种计算上可行的优化方法。最后，通过仿真实验证明了所提出方法的有效性。