Adaptive Generalized Student's t for AUV Navigation in Non-Gaussian Noise without Prior Knowledge

Reliable navigation of autonomous underwater vehicles (AUVs) depends on high-precision state estimation, while complex underwater environments render measurement data susceptible to interference from non-Gaussian noise, significantly degrading the performance of traditional filtering algorithms. This paper investigates nonlinear state estimation under heavy-tailed measurement noise using an improved generalized maximum correntropy criterion, especially in scenarios where the measurement noise characteristics are unknown. A generalized Student’s t maximum correntropy criterion is proposed to mitigate the impact of non-Gaussian noise disturbances in Doppler Velocity Log (DVL) measurements. Additionally, variational Bayesian inference is utilized to tackle the challenge posed by unknown prior characteristics. Subsequently, the mean error analysis and mean square error analysis of the proposed method are evaluated. Finally, the effectiveness of the algorithm is validated through numerical simulations and sea trial data.