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A Novel Integrated Inertial Navigation System with a Single-Axis Cold Atom Interferometer Gyroscope Based on

Zihao Chen1, Fangjun Qin1, Sibin Lu2

  • 1College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China.

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Summary
This summary is machine-generated.

This study integrates a cold atom interferometer gyroscope (CAIG) with a conventional inertial measurement unit (IMU) for improved navigation. The novel system significantly reduces navigation errors over 24 hours.

Keywords:
Kalman filteradaptive gradient ascentcold atom interferometer gyroscopeintegrated navigation system

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Area of Science:

  • Navigation Systems Engineering
  • Quantum Sensing Technology
  • Control Systems Theory

Background:

  • Inertial navigation systems (INSs) offer autonomous, covert, long-duration operation but accumulate errors over time.
  • Sensor limitations and operational principles contribute to cumulative errors in traditional INS.
  • Accurate navigation is crucial for various applications, necessitating error mitigation strategies.

Purpose of the Study:

  • To theoretically explore and numerically simulate an integrated inertial navigation system combining a cold atom interferometer gyroscope (CAIG) and a conventional inertial measurement unit (IMU).
  • To leverage the distinct advantages of CAIG (low bias, drift) and IMU (high sampling rate) for enhanced navigation accuracy.
  • To develop and implement an adaptive gradient ascent (AGA) method for online estimation of measurement noise variance in the Kalman filter.

Main Methods:

  • Integration of a single-axis cold atom interferometer gyroscope (CAIG) with a conventional inertial measurement unit (IMU).
  • Development of an adaptive gradient ascent (AGA) algorithm for online Kalman filter parameter tuning.
  • Numerical simulation of the integrated system over a 24-hour operational period.

Main Results:

  • The integrated system demonstrated significant reductions in navigation errors compared to a conventional IMU alone.
  • Latitude, longitude, and positioning errors were reduced by 43.9%, 32.6%, and 32.3%, respectively, over 24 hours.
  • The online Kalman filter effectively reduced errors stemming from inertial sensor drift.

Conclusions:

  • The proposed integrated inertial navigation system, combining CAIG and IMU, offers superior navigation accuracy.
  • The adaptive gradient ascent method enhances Kalman filter performance by accurately estimating noise variance.
  • This approach provides a viable solution for mitigating cumulative errors in long-duration autonomous navigation.