Adaptive maximum correntropy UKF TDOA tracking algorithm based on multi-layer isogradient sound speed profile

In this paper, a new adaptive maximum correntropy unscented Kalman filter (AMCUKF) Time-Difference-of-Arrival (TDOA) tracking algorithm is proposed for autonomous underwater vehicles (AUVs) in the complex and variable marine environment. Firstly, the measurement model of the acoustic tracking system is established based on the multi-layer isogradient sound speed profile (SSP) model to correct tracking errors caused by the bending of sound transmission trajectory. To resist the impact of measurement gross errors caused by instrument measurement anomalies or accidental environmental interference, the maximum correntropy unscented Kalman filter (MCUKF) TDOA tracking algorithm is constructed by introducing the maximum correntropy criterion into the UKF. Then, this paper introduces the innovation-based covariance-matching method and moving window method to improve the algorithm's adaptability, which can reduce the influence of covariance variable measurement noise caused by marine environmental changes. Finally, we compare the effectiveness of the proposed AMCUKF algorithm with the UKF algorithm, adaptive unscented Kalman filter (AUKF) algorithm, and MCUKF algorithm, which indicates the AMCUKF algorithm has better tracking performance in the marine environment with covariance variable measurement noise, measurement gross errors, and Gaussian mixture noise.