State Estimation Methods for Remote-Controlled Ships Considering Packet Delay, Reordering, and Loss in Ship-Shore Communication

Remote Control Maritime Autonomous Surface Ships (RC-MASS) have become promising in the near future, while the delivery of the perception data from the ship to the shore suffers from communication issues, such as packet delay, reordering, and loss. In order to improve the accuracy of RC-MASS state estimation in complex communication environments and ensure the performance of control, two effective real-time state estimation methods are proposed. First, a comprehensive model is established by integrating the state of acceleration into the traditional ship motion model; Secondly, a simplified augment CKF (A-CKF) framework is proposed, in which the system state is augment by the previous states; Third, the State Estimation Algorithm with Last Data (SEA-LD) and the SEA with Retrodicted States (SEA-RS) are proposed, which are built upon the A-CKF framework and incorporate a novel state update strategy to accurately estimate the ship’s true state and reduce the impact of accumulated state errors caused by packet delay, reordering, and loss. To validate the proposed method, real-ship and simulation experiments under different communication conditions are introduced. The results indicate that the proposed method has significant improvements in state estimation performance and control performance of RC-MASS under complex communication scenarios. Under a normal delay distribution with means of 200ms, 500ms, and 1000ms, a variance of 10% of the mean, and a packet loss rate of 0.1%, both the SEA-RS and SEA-LD algorithms achieve errors below 0.047 m/s in velocity and 1.25m in position. In terms of control performance, the proposed methods can maintain stable control under severe delay conditions of 2500ms.