Advances in Hypothesizing Distributed Kalman Filtering

被引:0
作者
Reinhardt, Marc [1 ]
Noack, Benjamin [1 ]
Hanebeck, Uwe D. [1 ]
机构
[1] Karlsruhe Inst Technol, Inst Anthropomat, Intelligent Sensor Actuator Syst Lab ISAS, D-76021 Karlsruhe, Germany
来源
2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2013年
关键词
Kalman Filtering; Distributed Estimation; Sensor-networks; Track-to-Track Fusion (T2TF); ALGORITHM;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this paper, linear distributed estimation is revisited on the basis of the hypothesizing distributed Kalman filter and equations for a flexible application of the algorithm are derived. We propose a new approximation for the mean-squared-error matrix and present techniques for automatically improving the hypothesis about the global measurement model. Utilizing these extensions, the precision of the filter is improved so that it asymptotically yields optimal results for time-invariant models. Pseudo-code for the implementation of the algorithm is provided and the lossless inclusion of out-of-sequence measurements is discussed. An evaluation demonstrates the effect of the new extensions and compares the results to state-of-the-art methods.
引用
收藏
页码:77 / 84
页数:8
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