论文标题
连接车辆的分布式互补融合
Distributed Complementary Fusion for Connected Vehicles
论文作者
论文摘要
我们提出了一种基于有限的设置方法，用于在分布式车辆网络中通过每辆车实现全面的情况意识。我们的解决方案是为在每辆车中运行的标有多重bernoulli过滤器而设计的。它涉及通过共识迭代本地运行的传感器信息的互补融合。我们介绍了一种新型标签合并算法以消除双重计数。我们还扩展了标签空间以结合传感器身份。这有助于克服标签不一致。我们表明，所提出的算法能够使用具有有限视野的分布式互补方法胜过标准LMB过滤器。
We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled multi-Bernoulli filters running in each vehicle. It involves complementary fusion of sensor information locally running through consensus iterations. We introduce a novel label merging algorithm to eliminate double counting. We also extend the label space to incorporate sensor identities. This helps to overcome label inconsistencies. We show that the proposed algorithm is able to outperform the standard LMB filter using a distributed complementary approach with limited fields of view.
