A Waterfall-Plot-Based Multi-Criteria Framework for X-Ray Pulsar Time-Delay Estimation in Multi-Scenario Celestial Remote Sensing and Navigation.
Saved in:
| Title: | A Waterfall-Plot-Based Multi-Criteria Framework for X-Ray Pulsar Time-Delay Estimation in Multi-Scenario Celestial Remote Sensing and Navigation. |
|---|---|
| Authors: | Xie, Tianhao1,2,3 (AUTHOR), Ma, Xin1,2,3 (AUTHOR) maxin@buaa.edu.cn, Yu, Wei3,4 (AUTHOR), Cui, Peiling1,2,3,4 (AUTHOR), Ning, Xiaolin1,2,3,5 (AUTHOR), Li, Jianli1,2,3,5,6 (AUTHOR), Zhang, Rong5,6,7 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1693. 33p. |
| Subjects: | Time delay estimation, Navigation (Astronautics), Support vector machines, Data visualization, Principal components analysis, Remote sensing, Multiple criteria decision making |
| Abstract: | Highlights: What are the main findings? A waterfall-plot-based multi-criteria framework is proposed for X-ray pulsar time-delay estimation, integrating structural saliency, principal-projection saliency, and geometric consistency for robust period discrimination. The proposed method significantly improves the accuracy and stability of X-ray pulsar time-delay estimation compared with benchmark statistical-test-based methods using Insight-HXMT satellite observation data. An end-to-end simulation framework is established for multi-scenario celestial remote sensing and navigation, enabling full-chain validation from photon observation to final navigation performance. What are the implications of the main findings? The proposed framework provides a robust way to extract pulsar measurement information from X-ray pulsar observations under complex observation conditions. Improved time-delay estimation can effectively enhance navigation performance in typical deep-space mission scenarios, including Earth orbit, Earth–Moon transfer, and Mars approach. The proposed method shows strong potential for long-duration autonomous deep-space remote sensing and navigation applications. To improve the accuracy and stability of X-ray pulsar time-delay estimation for multi-scenario celestial remote sensing and navigation, this paper proposes a time-delay estimation method based on a waterfall-plot multi-criteria framework and develops an end-to-end simulation framework for multi-scenario applications. First, a pulsar profile waterfall-plot model is built, and principal component analysis is performed to characterize candidate periodic structures. The contribution rate of the principal eigenvalue is used to describe the overall significance of the candidate period, and the projection variance of the first principal component is used to measure the prominence of the candidate pattern in the principal subspace. Second, support vector regression is used to fit the peak track of the waterfall plot, and a regression slope is used to describe the geometric stability of the candidate period. These three indicators are fused for pulsar period and time-delay estimation. Tests based on Insight-HXMT satellite observation data show that, compared with the χ 2 and Z 2 test methods, our method improves time-delay estimation accuracy by 68.68% and 50.43%, respectively. Multi-scenario navigation simulations indicate positioning improvements of approximately 0.83 km, 3.04 km, and 1.05 km in the Earth-orbiting, Earth–Moon transfer, and Mars approach scenarios, respectively. These results suggest that the proposed framework can improve pulsar time-delay estimation and may provide useful measurement support for celestial remote sensing and navigation. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A waterfall-plot-based multi-criteria framework is proposed for X-ray pulsar time-delay estimation, integrating structural saliency, principal-projection saliency, and geometric consistency for robust period discrimination. The proposed method significantly improves the accuracy and stability of X-ray pulsar time-delay estimation compared with benchmark statistical-test-based methods using Insight-HXMT satellite observation data. An end-to-end simulation framework is established for multi-scenario celestial remote sensing and navigation, enabling full-chain validation from photon observation to final navigation performance. What are the implications of the main findings? The proposed framework provides a robust way to extract pulsar measurement information from X-ray pulsar observations under complex observation conditions. Improved time-delay estimation can effectively enhance navigation performance in typical deep-space mission scenarios, including Earth orbit, Earth–Moon transfer, and Mars approach. The proposed method shows strong potential for long-duration autonomous deep-space remote sensing and navigation applications. To improve the accuracy and stability of X-ray pulsar time-delay estimation for multi-scenario celestial remote sensing and navigation, this paper proposes a time-delay estimation method based on a waterfall-plot multi-criteria framework and develops an end-to-end simulation framework for multi-scenario applications. First, a pulsar profile waterfall-plot model is built, and principal component analysis is performed to characterize candidate periodic structures. The contribution rate of the principal eigenvalue is used to describe the overall significance of the candidate period, and the projection variance of the first principal component is used to measure the prominence of the candidate pattern in the principal subspace. Second, support vector regression is used to fit the peak track of the waterfall plot, and a regression slope is used to describe the geometric stability of the candidate period. These three indicators are fused for pulsar period and time-delay estimation. Tests based on Insight-HXMT satellite observation data show that, compared with the χ 2 and Z 2 test methods, our method improves time-delay estimation accuracy by 68.68% and 50.43%, respectively. Multi-scenario navigation simulations indicate positioning improvements of approximately 0.83 km, 3.04 km, and 1.05 km in the Earth-orbiting, Earth–Moon transfer, and Mars approach scenarios, respectively. These results suggest that the proposed framework can improve pulsar time-delay estimation and may provide useful measurement support for celestial remote sensing and navigation. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111693 |
