Relative Localization Error Correction Method Based on SDP-Optimized Range Measurements

XING Jiayi; DENG Kai; WANG Changhong; RAO Liang

doi:10.11993/j.issn.2096-3920.2025-0099

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XING Jiayi, DENG Kai, WANG Changhong, RAO Liang. Relative Localization Error Correction Method Based on SDP-Optimized Range Measurements[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0099

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XING Jiayi, DENG Kai, WANG Changhong, RAO Liang. Relative Localization Error Correction Method Based on SDP-Optimized Range Measurements[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0099

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Relative Localization Error Correction Method Based on SDP-Optimized Range Measurements

doi: 10.11993/j.issn.2096-3920.2025-0099

XING Jiayi^{1, 2
,},
DENG Kai^{1, 3
,},
WANG Changhong^{1, 3},
RAO Liang^{1, 3}

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Beijing Engineering Technology Research Center of Ocean Acoustics Equipment, Beijing 100190, China

Received Date: 2025-07-31
Accepted Date: 2025-09-08
Rev Recd Date: 2025-09-02

Available Online: 2025-12-03

Abstract

Abstract

Most of the existing researches on cooperative localization focus on absolute position estimation, with relatively little attention paid to the relative positioning among nodes in clusters. To simplify the ranging model and provide a more intuitive evaluation of algorithm performance, this paper assumes that nodes within the cluster remain relatively stationary, and proposes an inertial navigation system (INS) error correction method based on semidefinite programming (SDP) optimized ranging. At the time of ranging, SDP optimization is performed on the original ranging data to correct the positioning error of the INS, followed by error compensation updates for the inherent biases of the INS, which effectively enhances the relative positioning accuracy among the nodes. The paper establishes a localization model for the cluster system, derives a nonlinear least squares formulation for node positioning, and provides an analysis of INS error compensation. Multiple simulation results indicate that, compared with the method of directly using the original ranging data to correct the INS, the proposed method can reduce the relative positioning error by more than 40% on average, and its advantage in suppressing INS errors becomes even more pronounced as the ranging update interval increases.
- semidefinite programming,
- relative localization,
- error correction

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References(18)

References

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