Dormann Katharina, Noack Benjamin, Hanebeck Uwe D
Robert Bosch GmbH, 71636 Ludwigsburg, Germany.
Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
Sensors (Basel). 2018 Mar 29;18(4):1034. doi: 10.3390/s18041034.
For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested. In this article, different extensions of the optimally distributed Kalman filter are proposed that employ data-driven transmission schemes in order to reduce communication expenses. As a first relaxation of the full-rate communication scheme, it can be shown that each node only has to transmit every second time step without endangering consistency of the fusion result. Also, two data-driven algorithms are introduced that even allow for lower transmission rates, and bounds are derived to guarantee consistent fusion results. Simulations demonstrate that the data-driven distributed filtering schemes can outperform a centralized Kalman filter that requires each measurement to be sent to the center node.
对于多传感器数据融合，能够对传感器数据进行本地处理的分布式状态估计技术是首选方法，以便将存储和通信成本降至最低。特别是，最近开发了最优卡尔曼滤波器的分布式实现。该算法的一个显著缺点是融合中心需要访问每个节点才能计算一致的状态估计，这在每次请求估计时都需要进行完全通信。在本文中，提出了最优分布式卡尔曼滤波器的不同扩展，这些扩展采用数据驱动的传输方案以减少通信开销。作为全速率通信方案的首次放宽，可以证明每个节点只需每隔一个时间步进行传输，而不会危及融合结果的一致性。此外，还引入了两种数据驱动算法，它们甚至允许更低的传输速率，并推导出界限以保证一致的融合结果。仿真表明，数据驱动的分布式滤波方案可以优于需要将每个测量值发送到中心节点的集中式卡尔曼滤波器。
