@inproceedings{283eda6806f349b58d80496a7eaf598c,
title = "Distributed Kalman filtering in the presence of packet delays and losses",
abstract = "Distributed Kalman filtering aims at optimizing an estimate at a fusion center based on information that is gathered in a sensor network. Recently, an exact solution based on local estimation tracks has been proposed and an extension to cope with packet losses has been derived. In this contribution, we generalize both algorithms to packet delays. The key idea is to introduce augmented measurement vectors in the sensors that permit the optimization of local filter gains according to time-dependent measurement capabilities at the fusion center. In the most general form, the algorithm provides optimized estimates in sensor networks with packets delays and losses. The precision depends on the actual arrival patterns, and the results correspond to those of the centralized Kalman filter when specific assumptions about the measurement capability are satisfied.",
keywords = "Distributed Estimation, Kalman Filtering, Target Tracking",
author = "Marc Reinhardt and Benjamin Noack and Sanjeev Kulkarni and Hanebeck, {Uwe D.}",
note = "Publisher Copyright: {\textcopyright} 2014 International Society of Information Fusion.; 17th International Conference on Information Fusion, FUSION 2014 ; Conference date: 07-07-2014 Through 10-07-2014",
year = "2014",
month = oct,
day = "3",
language = "English (US)",
series = "FUSION 2014 - 17th International Conference on Information Fusion",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "FUSION 2014 - 17th International Conference on Information Fusion",
address = "United States",
}