Adaptive Dual-Bias Estimation for Hybrid Inertial Navigation with Single-Axis Cold Atom Interferometric Gyroscope

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Abstract
The errors of an inertial navigation system accumulate over time due to its operating principles and sensor limitations. Numerous studies have investigated hybrid inertial navigation schemes combining a cold atom interferometric gyroscope (CAIG) and a conventional inertial measurement unit. This paper constructs a dual-bias joint state model to realize decoupling of gyroscope biases between the conventional gyroscope and the CAIG, eliminating modeling error introduced by the drift-free reference sensor assumption. Furthermore, an adaptive Kalman filtering algorithm based on real-time residual autocorrelation identification is proposed to cope with the temporally correlated time-varying noise characteristics. A series of simulations including noise-characteristic verification and bias-decoupling comparison, as well as static physical experiments, are carried out to validate the bias-decoupling capability and overall navigation performance. Taking advantage of the low bias-drift property of the CAIG and the high sampling rate of conventional gyroscopes, static experimental results show that the hybrid navigation system with a single-axis CAIG reduces positioning errors by more than 85% after compensating for inertial sensor biases, and achieves a positioning accuracy of 5 nautical miles over a one-hour test. Although the current validation is limited to stationary conditions, this work provides a feasible solution for CAIG-aided hybrid inertial navigation.
Funding Information
  • Department of Science and Technology of Hubei Province (2025BCB113)

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