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Gravity-Matching Algorithm Based on K-Nearest Neighbor.

Shuaipeng Gao1, Tijing Cai1, Ke Fang1

  • 1School of Instrument Science and Engineering, Southeast University, 2 Sipailou, Nanjing 210096, China.

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|June 24, 2022
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Summary
This summary is machine-generated.

A new gravity-matching algorithm using K-Nearest Neighbors (KNN) improves gravity-aided navigation accuracy and anti-noise capabilities. This method enhances positioning accuracy for inertial navigation systems (INS), making it suitable for real-time applications.

Keywords:
Eotvos effectK-nearest neighbordistance weightgravity matchingintegrated navigationspeed constraint

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Area of Science:

  • Geophysics
  • Navigation Systems
  • Data Science

Background:

  • Gravity-aided inertial navigation systems (INS) leverage geophysical data for enhanced positioning.
  • Gravity-map-matching algorithms are crucial for the accuracy of these systems.
  • Existing algorithms face challenges with noise and accuracy limitations.

Purpose of the Study:

  • To introduce a novel gravity-matching algorithm based on K-Nearest Neighbors (KNN).
  • To improve the anti-noise capability and accuracy of gravity-aided navigation.
  • To reduce the application threshold for gravity-matching algorithms.

Main Methods:

  • A KNN-based gravity-matching algorithm is proposed.
  • K sample labels are selected using Euclidean distance between measurements and data.
  • Weighted averaging of labels, considering spatial position and sailing speed constraints, is used for continuous navigation results.

Main Results:

  • Simulation experiments demonstrate the algorithm's effectiveness in reducing INS positioning error.
  • Position errors in both longitude and latitude directions were less than 800 meters.
  • The algorithm exhibits enhanced stability and anti-noise capability during continuous matching.

Conclusions:

  • The proposed KNN-based gravity-matching algorithm effectively reduces INS positioning errors.
  • The algorithm meets real-time navigation requirements with an average KNN running time of 5.87s per matching point.
  • This approach offers improved accuracy and robustness for gravity-aided navigation.