Performance of state estimation and fusion with elliptical motion constraints

Qiang Liu; Nageswara S.V. Rao

doi:10.1007/978-3-319-90509-9_3

Performance of state estimation and fusion with elliptical motion constraints

Qiang Liu, Nageswara S.V. Rao

Autonomous and Complex Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We consider tracking of a target with known elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to the center, and (ii) shortest distance to the ellipse are discussed. A tracking example is used to illustrate the tracking performance using projection-based methods with various fusers in a lossy long-haul tracking environment.

Original language	English
Title of host publication	Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017
Editors	Hanseok Ko, Sukhan Lee, Songhwai Oh
Publisher	Springer Verlag
Pages	39-51
Number of pages	13
ISBN (Print)	9783319905082
DOIs	https://doi.org/10.1007/978-3-319-90509-9_3
State	Published - 2018
Event	13th IEEE International Conference on Multisensor Integration and Fusion, IEEE MFI 2017 - Daegu, Korea, Republic of Duration: Nov 16 2017 → Nov 22 2017

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	501
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	13th IEEE International Conference on Multisensor Integration and Fusion, IEEE MFI 2017
Country/Territory	Korea, Republic of
City	Daegu
Period	11/16/17 → 11/22/17

Funding

Acknowledgments. This work was funded by the Mathematics of Complex, Distributed, Interconnected Systems Program, Office of Advanced Computing Research, U.S. Department of Energy, and SensorNet Project of Office of Naval Research, and was performed at Oak Ridge National Laboratory managed by UT-Battelle, LLC for U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Funders	Funder number
Office of Advanced Computing Research
SensorNet Project of Office of Naval Research
U.S. Department of Energy
Oak Ridge National Laboratory
UT-Battelle	DE-AC05-00OR22725

Keywords

Elliptical track constraints
Error covariance matrices
Long-haul sensor networks
Nonlinear constraints
Projection
Root-mean-square-error (RMSE) performance
State estimate fusion

Access to Document

10.1007/978-3-319-90509-9_3

Cite this

Liu, Q., & Rao, N. S. V. (2018). Performance of state estimation and fusion with elliptical motion constraints. In H. Ko, S. Lee, & S. Oh (Eds.), Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017 (pp. 39-51). (Lecture Notes in Electrical Engineering; Vol. 501). Springer Verlag. https://doi.org/10.1007/978-3-319-90509-9_3

Liu, Qiang ; Rao, Nageswara S.V. / Performance of state estimation and fusion with elliptical motion constraints. Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017. editor / Hanseok Ko ; Sukhan Lee ; Songhwai Oh. Springer Verlag, 2018. pp. 39-51 (Lecture Notes in Electrical Engineering).

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abstract = "We consider tracking of a target with known elliptical nonlinear constraints on its motion dynamics. The state estimates are generated by sensors and sent over long-haul links to a remote fusion center for fusion. We show that the constraints can be projected onto the ellipse and hence incorporated into the estimation and fusion process. In particular, two methods based on (i) direct connection to the center, and (ii) shortest distance to the ellipse are discussed. A tracking example is used to illustrate the tracking performance using projection-based methods with various fusers in a lossy long-haul tracking environment.",

keywords = "Elliptical track constraints, Error covariance matrices, Long-haul sensor networks, Nonlinear constraints, Projection, Root-mean-square-error (RMSE) performance, State estimate fusion",

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Liu, Q & Rao, NSV 2018, Performance of state estimation and fusion with elliptical motion constraints. in H Ko, S Lee & S Oh (eds), Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017. Lecture Notes in Electrical Engineering, vol. 501, Springer Verlag, pp. 39-51, 13th IEEE International Conference on Multisensor Integration and Fusion, IEEE MFI 2017, Daegu, Korea, Republic of, 11/16/17. https://doi.org/10.1007/978-3-319-90509-9_3

Performance of state estimation and fusion with elliptical motion constraints. / Liu, Qiang; Rao, Nageswara S.V.
Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017. ed. / Hanseok Ko; Sukhan Lee; Songhwai Oh. Springer Verlag, 2018. p. 39-51 (Lecture Notes in Electrical Engineering; Vol. 501).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Liu Q, Rao NSV. Performance of state estimation and fusion with elliptical motion constraints. In Ko H, Lee S, Oh S, editors, Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System - An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI 2017. Springer Verlag. 2018. p. 39-51. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-3-319-90509-9_3

Performance of state estimation and fusion with elliptical motion constraints

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