Chingiz Hajiyev, Ulviye Hacizade

A Covariance Matching-Based Adaptive Measurement Differencing Kalman Filter for INS’s Error Compensation

  • Artificial Intelligence
  • General Mathematics
  • Control and Systems Engineering
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In this study, a covariance matching-based adaptive measurement differencing Kalman filter (AMDKF) for the case of time-correlated measurement errors is proposed. The solution to the state estimation problem involves deriving a filter that accounts for measurement differences. Specifically, the measurement noise in the generated measurements is assumed to be correlated with the process noise. To address this issue in the context of correlated process and measurement noise, we propose an adaptive measurement differencing Kalman filter that is robust to measurement faults. We also evaluate the robustness of the suggested AMDKF through an analysis. When noise increment type sensor faults are present in the time-correlated inertial navigation systems (INS) measurements, the states of a multi-input/output aircraft model were estimated using both the previously developed measurement differencing Kalman filter (MDKF) and the suggested AMDKF and the results were compared.

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