Aiming at the problem of low positioning accuracy of underwater target, this paper proposes unscented Kalman filter (UKF), which is a common AUV weighted statistical linear regression track tracking algorithm. Its algorithm redundancy is lower than EKF, PF, PSO and other numerical optimization algorithms, and the algorithm efficiency is higher. However, when using UKF filtering algorithm to estimate AUV state variables using UKF, the prediction noise covariance and observation noise covariance are usually set to a certain value, and the AUV motion state is a nonlinear system. With the movement of AUV, the number of iterations of the algorithm increases, which will cause the accumulation of errors, and even lead to the divergence of positioning errors. Therefore, the improved adaptive Kalman filtering algorithm in this paper adds the adaptive adjustment of the process noise covariance matrix Q and observation noise covariance matrix R to the nonlinear filtering system, the aukf filtering can better suppress the decline of filtering accuracy or even divergence, and reduce the cumulative error of the prediction algorithm. Finally, the simulation results show that the target tracking accuracy is effectively improved and the influence of process noise and sensor noise on the prediction process is reduced.

Xin Li, Taoli Yuan, Guo Tuo, Siyu Zhang, Xi Zhao, Zhuliang Li. Research on AUV positioning and mapping based on improved adaptive Kalman filter. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 12: 59-65. https://doi.org/10.25236/AJCIS.2024.071208.

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