@inproceedings{6355271620e941539313ee76d5f8d688,
title = "Nonlinear distributed estimation fusion that reduces mean square error",
abstract = "This paper considers distributed estimation in multisensor tracking systems with and without knowledge about crosscovariance matrices among the local estimation errors. Nonlinear fusion rules are proposed to reduce the mean square error (MSE) of the estimate. Based on the best linear unbiased estimation fusion and covariance intersection fusion formulas, several classes of nonlinear estimators are proposed, which have a lower MSE than existing linear unbiased fusers. Some numerical examples are provided to verify the theoretical analysis and to illustrate the performance of the proposed estimators.",
keywords = "Distributed fusion, Least squares, Mean square error, Nonlinear estimation",
author = "Hua Li and Feng Xiao and Jie Zhou and Li, \{X. Rong\}",
note = "Publisher Copyright: {\textcopyright} 2013 ISIF.; 16th International Conference of Information Fusion, FUSION 2013 ; Conference date: 09-07-2013 Through 12-07-2013",
year = "2013",
language = "英语",
isbn = "9786058631113",
series = "Proceedings of the 16th International Conference on Information Fusion, FUSION 2013",
publisher = "IEEE Computer Society",
pages = "2200--2206",
booktitle = "Proceedings of the 16th International Conference on Information Fusion, FUSION 2013",
}