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Optimal Multiple-Sensor Scheduling for General Scalar Gauss-Markov Systems with the Terminal Error

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Cognitive Systems and Signal Processing (ICCSIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 710))

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Abstract

In this work, we study finite-horizon multiple-sensor scheduling for general scalar Gauss-Markov systems, extending previous results where only a class of systems are considered. The scheduling objective is to minimize the terminal estimation error covariance. Only one sensor can transmit its measurement per time instant and each sensor has limited energy. Through building a comparison function and solving its monotone intervals, an efficient algorithm is designed to construct the optimal schedule.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under grant numbers 61333011, 61271144, 61371064 and 61603133.

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Authors and Affiliations

  1. School of Automation, Institute of Systems Science and Control Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Jiapeng Xu & Chenglin Wen

  2. College of Electrical and Information Engineering, Quzhou University, Quzhou, 324000, China

    Daxing Xu

  3. School of Engineering, Huzhou University, Huzhou, 313000, China

    Huiying Chen

Authors
  1. Jiapeng Xu
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  2. Chenglin Wen
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  3. Daxing Xu
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  4. Huiying Chen
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Corresponding author

Correspondence to Chenglin Wen .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, China

    Dewen Hu

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Xu, J., Wen, C., Xu, D., Chen, H. (2017). Optimal Multiple-Sensor Scheduling for General Scalar Gauss-Markov Systems with the Terminal Error. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_44

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  • DOI: https://doi.org/10.1007/978-981-10-5230-9_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5229-3

  • Online ISBN: 978-981-10-5230-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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