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A SINS/DVL Integrated Navigation Method Based on EIMM-ARCKF Algorithm
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DOI：10.1109/JSEN.2024.3408461.png)
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Abstract
En 中文
In complex underwater environments, the strapdown inertial navigation system (SINS)/Doppler velocity logger (DVL) integrated navigation system often experiences accuracy degradation due to uncertain noise statistics and the impact of measurement outliers. To effectively address these challenges, this article proposes an adaptive robust cubature Kalman-assisted extended interactive multi-model algorithm (EIMM-ARCKF). This algorithm utilizes an adaptive adjustment strategy for the model set, enabling effective estimation of measurement noise statistics and real-time correction of the model probability transfer matrix. Additionally, it constructs an adaptive robustness factor for refining the measurement covariance, thereby enhancing the algorithm's robustness. Comparative analysis through simulations and Yangtze River experiments against representative adaptive robust filtering algorithms shows that the proposed EIMM-ARCKF offers superior estimation accuracy and robustness.
Keywords:
Noise
Adaptation models
Navigation
Noise measurement
Estimation
Measurement uncertainty
Sea measurements
Adaptive robust cubature Kalman filter (ARCKF)
autonomous underwater vehicle (AUV)
cubature Kalman filtering (CKF)
extended interactive multi-model (EIMM)
integrated navigation system
interactive multi-model (IMM)
outlier interference
strapdown inertial navigation system (SINS)/Doppler velocity logger (DVL)
time-varying statistical characteristics
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