Geomagnetic navigation, as an autonomous and passive navigation method without time accumulation error and less susceptible to interference, can navigate in any environment for a long time. However, the ICCP (Iterative Closest Contour Point) algorithm has the disadvantages of high sensor performance requirements, low noise tolerance, high algorithm complexity, and poor real-time performance. At the same time, MAGCOM (Magnetic Contour Matching) is limited by the size of the search area and cannot effectively correct the heading error. In the process of integrated navigation, the problem of large search area or discontinuous matching occurs. To solve the above problems, a geomagnetic navigation method utilizing an adaptive search area and a piecewise matching trajectory smoothing strategy has been proposed. The experimental findings indicate that the matching precision of the algorithm can reach about 200 m, which effectively realizes the matching accuracy and real-time performance.

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Inertial-Geomagnetic Integrated Navigation Method Based on Kalman Filter and Adaptive Search Area

  • Qinghua Luo,
  • Boyuan Liu,
  • Mutong Yu,
  • Yuhao Su,
  • Longxin Yang,
  • Lan Wang

摘要

Geomagnetic navigation, as an autonomous and passive navigation method without time accumulation error and less susceptible to interference, can navigate in any environment for a long time. However, the ICCP (Iterative Closest Contour Point) algorithm has the disadvantages of high sensor performance requirements, low noise tolerance, high algorithm complexity, and poor real-time performance. At the same time, MAGCOM (Magnetic Contour Matching) is limited by the size of the search area and cannot effectively correct the heading error. In the process of integrated navigation, the problem of large search area or discontinuous matching occurs. To solve the above problems, a geomagnetic navigation method utilizing an adaptive search area and a piecewise matching trajectory smoothing strategy has been proposed. The experimental findings indicate that the matching precision of the algorithm can reach about 200 m, which effectively realizes the matching accuracy and real-time performance.