An adaptive Kalman filtering algorithm for ship speed measurement

In response to the problem of low measurement accuracy caused by dynamic model errors in ship speed measurement using Kalman filtering, this article proposes an adaptive Kalman filtering algorithm: an abnormal error identification function is constructed to detect dynamic model errors, and an exponential function is used to construct an adaptive factor to reduce dynamic model errors. Simulation verification shows that compared with KF algorithm and interactive multiple model algorithm (IMM), AKF algorithm has reduced the mean absolute error (MAE) by 22% and 11% respectively, and the root mean square error (RSME) by 26% and 15% respectively, indicating that AKF algorithm has better performance in both measurement accuracy and stability evaluation indicators.