Target Tracking and Data Fusion in Sensor Networks Peer reviewed

Distributed estimation for range difference tracking without noise statistical characteristic

Shuwang Du, Xifeng Wang, Rusheng Wang, Haiyu Song

Transactions of the Institute of Measurement and Control | Jul 18, 2026

Abstract

This article is concerned with the distributed target tracking problem with range-only measurement, where the statistical characteristics of the system noises are unknown. First, the nonlinear measurement equation is transformed into a linear model using a type of nonlinear transformation, which can avoid the linearization errors caused by the Taylor expansion and unscented transform. The noise statistical characteristic of the converted measurement noise is still unknown, but it can be derived as bounded noise. Then, by minimizing the upper bound of estimation error, the spectral radius inequalities are constructed to determine the optimal estimator gain sequence. Meanwhile, the stability determination is derived during the estimator design. Finally, a target tracking example is presented to show the effectiveness of the proposed method.

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Authors

Researchers on this paper

Shuwang Du

first | Zhejiang University of Technology | ORCID 0000-0002-9170-2125

Xifeng Wang

middle | Zhejiang University of Finance and Economics | ORCID 0000-0002-4521-9407

Rusheng Wang

middle | Zhejiang University of Technology | ORCID 0000-0003-0953-6998

Haiyu Song

last | Zhejiang University of Finance and Economics | ORCID 0000-0003-4772-2855

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Latest Target Tracking and Data Fusion in Sensor Networks research Latest Indoor and Outdoor Localization Technologies research Distributed Sensor Networks and Detection Algorithms

Citation

BibTeX

@article{Du2026Distributed,
  title = {Distributed estimation for range difference tracking without noise statistical characteristic},
  author = {Shuwang Du and Xifeng Wang and Rusheng Wang and Haiyu Song},
  journal = {Transactions of the Institute of Measurement and Control},
  year = {2026},
  doi = {10.1177/01423312261463301},
  url = {https://doi.org/10.1177/01423312261463301}
}

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