A Robust and Adaptive AUV Integrated Navigation Algorithm Based on a Maximum Correntropy Criterion

In the underwater domain where Autonomous Underwater Vehicles (AUVs) operate, measurements may suffer from the impact of outliers and non-Gaussian noise. These factors can potentially undermine the efficacy of integrated navigation algorithms. The Maximum Correntropy Criterion (MCC) can be utilized to enhance the robustness of AUV integrated navigation algorithms through the construction and maximization of the correntropy function. Notwithstanding, the underwater environment occasionally presents unknown time-varying noise, a situation for which the MCC lacks adaptability. In response to this issue, our study introduces a novel integrated navigation algorithm that synergizes the MCC and the Variational Bayesian approach, thereby augmenting both the robustness and adaptability of the system. Initially, we implement the MCC along with a mixture kernel function in an Unscented Kalman Filter (UKF) to strengthen the robustness of the AUV integrated navigation algorithms amidst the complexities inherent to underwater environmental conditions. Additionally, we utilize the Variational Bayesian method to refine the approximation of measurement noise covariance, thereby boosting the algorithm's adaptability to fluctuating scenarios. We evaluate the performance of our proposed algorithm using both simulation and sea trial datasets. The experimental results reveal a significant enhancement in the Root Mean Square Error (RMSE) and navigation accuracy of our proposed algorithm. Notably, in a complex noise environment, our algorithm achieves, approximately, a 50% improvement in navigation accuracy over other established algorithms.