Optimal estimation of sensor biases for asynchronous multi-sensor data fusion

Optimal estimation of sensor biases for asynchronous multi-sensor data fusion Math. Program., Ser. B https://doi.org/10.1007/s10107-018-1304-2 FULL LENGTH PAPER Optimal estimation of sensor biases for asynchronous multi-sensor data fusion 1 2 1 1 Wenqiang Pu · Ya-Feng Liu · Junkun Yan · Hongwei Liu · Zhi-Quan Luo Received: 12 March 2018 / Accepted: 19 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018 Abstract An important step in a multi-sensor surveillance system is to estimate sen- sor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares formula- tion for the problem by assuming the existence of a reference target moving with an This work was performed at the Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen. It is funded in part by a National Natural Science Foundation of China (NSFC) Key Project Grant 61731018, by NSFC Grants 11331012, 11631013, and 61601340, and in part by the China National Funds for Distinguished Young Scientists Grant 61525105. B Zhi-Quan Luo luozq@cuhk.edu.cn Wenqiang Pu wqpu@stu.xidian.edu.cn Ya-Feng Liu http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mathematical Programming Springer Journals

Optimal estimation of sensor biases for asynchronous multi-sensor data fusion

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society
Subject
Mathematics; Calculus of Variations and Optimal Control; Optimization; Mathematics of Computing; Numerical Analysis; Combinatorics; Theoretical, Mathematical and Computational Physics; Mathematical Methods in Physics
ISSN
0025-5610
eISSN
1436-4646
D.O.I.
10.1007/s10107-018-1304-2
Publisher site
See Article on Publisher Site

Abstract

Math. Program., Ser. B https://doi.org/10.1007/s10107-018-1304-2 FULL LENGTH PAPER Optimal estimation of sensor biases for asynchronous multi-sensor data fusion 1 2 1 1 Wenqiang Pu · Ya-Feng Liu · Junkun Yan · Hongwei Liu · Zhi-Quan Luo Received: 12 March 2018 / Accepted: 19 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018 Abstract An important step in a multi-sensor surveillance system is to estimate sen- sor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares formula- tion for the problem by assuming the existence of a reference target moving with an This work was performed at the Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen. It is funded in part by a National Natural Science Foundation of China (NSFC) Key Project Grant 61731018, by NSFC Grants 11331012, 11631013, and 61601340, and in part by the China National Funds for Distinguished Young Scientists Grant 61525105. B Zhi-Quan Luo luozq@cuhk.edu.cn Wenqiang Pu wqpu@stu.xidian.edu.cn Ya-Feng Liu

Journal

Mathematical ProgrammingSpringer Journals

Published: May 31, 2018

References

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