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The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
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Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System
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A Strap-Down Inertial Navigation/Spectrum Red-Shift/Star Sensor (SINS/SRS/SS) Autonomous Integrated System for

Zhaohui Gao1, Dejun Mu2, Yongmin Zhong3

  • 1School of Automatics, Northwestern Polytechnical University, Xi'an 710072, China. alexandergao@mail.nwpu.edu.cn.

Sensors (Basel, Switzerland)
|June 29, 2018
PubMed
Summary

This study introduces a new Strap-down Inertial Navigation System/Spectrum Red-Shift/Star Sensor (SINS/SRS/SS) integration method. This approach enhances spacecraft navigation autonomy and reliability by utilizing celestial spectrum red-shift data.

Keywords:
SINS/SRS/SS integrated navigation systemrobust adaptive unscented particle filterspacecraft navigationspectral red-shift

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Area of Science:

  • Aerospace Engineering
  • Astrodynamics
  • Navigation Systems

Background:

  • Spacecraft navigation relies on accurate and reliable systems.
  • Current methods may face limitations in autonomy and robustness.
  • Integrating multiple sensor types can enhance navigation performance.

Purpose of the Study:

  • To develop a novel system integration methodology for Strap-down Inertial Navigation System/Spectrum Red-Shift/Star Sensor (SINS/SRS/SS).
  • To improve the autonomy and reliability of spacecraft navigation.
  • To leverage spectrum red-shift information from celestial bodies for navigation.

Main Methods:

  • Established system models for SINS/SRS/SS integration.
  • Designed information fusion using a federated Kalman filter structure.
  • Developed a robust adaptive unscented particle filter for parallel local state estimations.

Main Results:

  • The proposed SINS/SRS/SS integration methodology effectively calculates navigation solutions.
  • Demonstrated enhanced autonomy in spacecraft navigation.
  • Achieved high reliability in navigation solutions through data fusion.

Conclusions:

  • The novel SINS/SRS/SS integration methodology significantly enhances spacecraft navigation.
  • The use of spectrum red-shift data combined with SINS and SS improves system robustness.
  • The federated Kalman filter and adaptive particle filter contribute to reliable state estimation.