Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion †
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Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute of Anthropomatics and Robotics (IAR), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
2
Autonomous Multisensor Systems Group (AMS), Institute for Intelligent Cooperating Systems (ICS), Otto von Guericke University Magdeburg (OVGU), 39106 Magdeburg, Germany
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Funk, C.; Noack, B.; Hanebeck, U.D. Conservative Quantization of Fast Covariance Intersection. In proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration (MFI 2020), Karlsruhe, Germany, 14–16 September 2020.
Academic Editor: Javier Bajo
Sensors 2021, 21(9), 3059; https://doi.org/10.3390/s21093059
Received: 25 March 2021
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Revised: 21 April 2021
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Accepted: 22 April 2021
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Published: 28 April 2021
(This article belongs to the Special Issue Multisensor Fusion and Integration)
Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted. Compression of estimates and covariance matrices can endanger desirable properties like unbiasedness and may lead to unreliable fusion results. In this work, quantization methods for estimates and covariance matrices are presented and their usage with the optimal fusion formulas and covariance intersection is demonstrated. The proposed quantization methods significantly reduce the bandwidth required for data transmission while retaining unbiasedness and conservativeness of the considered fusion methods. Their performance is evaluated using simulations, showing their effectiveness even in the case of substantial data reduction.
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Keywords:
covariance quantization; decentralized estimation; conservative fusion; covariance intersection; optimal fusion
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MDPI and ACS Style
Funk, C.; Noack, B.; Hanebeck, U.D. Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion. Sensors 2021, 21, 3059. https://doi.org/10.3390/s21093059
AMA Style
Funk C, Noack B, Hanebeck UD. Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion. Sensors. 2021; 21(9):3059. https://doi.org/10.3390/s21093059
Chicago/Turabian StyleFunk, Christopher, Benjamin Noack, and Uwe D. Hanebeck. 2021. "Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion" Sensors 21, no. 9: 3059. https://doi.org/10.3390/s21093059
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