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Distributed Dimensionality Reduction Fusion Kalman Filtering With Quantized Innovations

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Abstract

This paper is concerned with the distributed fusion Kalman filtering problem for networked systems with communication constraints. A dimensionality reduction strategy and a uniform quantization strategy are introduced to reduce communication traffic. To overcome the unboundedness of estimates/measurements in unstable systems, it is proposed to quantize the innovations that are sent to the fusion center through limited bandwidth channels. Then, a recursively distributed dimensionality reduction fusion Kalman filtering algorithm is developed by using a model uncertainty method to process quantization noises. Finally, a target tracking system is employed to demonstrate the effectiveness of the proposed methods.

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Data Availability Statement

All data included in this study are available upon request by contacting the corresponding author.

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Authors and Affiliations

  1. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Xinhao Yan & Xiang Qiu

  2. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Bo Chen

Authors
  1. Xinhao Yan
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  2. Bo Chen
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  3. Xiang Qiu
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Correspondence to Xiang Qiu.

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This work was supported in part by the National Natural Science Funds of China under Grant 61973277 and Grant 62073292 and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR20F030004.

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Yan, X., Chen, B. & Qiu, X. Distributed Dimensionality Reduction Fusion Kalman Filtering With Quantized Innovations. Circuits Syst Signal Process 40, 5234–5247 (2021). https://doi.org/10.1007/s00034-021-01707-8

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  • DOI: https://doi.org/10.1007/s00034-021-01707-8

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