Treatment of dependent information in multisensor kalman filtering and data fusion
Treatment of dependent information in multisensor kalman filtering and data fusion
复制标题
多传感器卡尔曼滤波和数据融合中相关信息的处理
DOI：
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发表时间：
2017
期刊：
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通讯作者：
U. Hanebeck
中科院分区：
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作者：
B. Noack;J. Sijs;Marc Reinhardt;U. Hanebeck
传感器数据的分布式和分散式处理和融合变得越来越重要。针对物联网和泛在感知的愿景，设计和实现多传感器状态估计算法已经成为一个关键问题。互连的传感器设备的网络的特征通常在于在传感器节点上本地且独立地处理和收集数据的想法。然而，这并不意味着数据是彼此独立的，并且状态估计算法必须解决可能的相互依赖性，以避免错误的数据融合结果。© 2016 by Taylor &弗朗西斯集团，有限责任公司CRC出版社是泰勒和弗朗西斯集团，一个Informa业务的印记。
Distributed and decentralized processing and fusion of sensor data are becoming increasingly important. In view of the Internet of Things and the vision of ubiquitous sensing, designing and implementing multisensor state estimation algorithm have already become a key issue. A network of interconnected sensor devices is usually characterized by the idea to process and collect data locally and independently on the sensor nodes. However, this does not imply that the data are independent of each other, and the state estimation algorithms have to address possible interdependencies so as to avoid erroneous data fusion results. © 2016 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business.