Due to the lack of information of Automatic Identification System (AIS) equipment, the location of a vessel cannot be accurately judged by intelligent supporting command system based on AIS. It is difficult to accurately issue the traffic signal from it. Meanwhile, due to the narrow and winding features in controlled waterway, it is difficult for traditional Kalman filter to accurately predict track of moving vessel. In this situation, the real-time estimation of system noise in Kalman filter algorithm was proposed to increase the accuracy of track prediction of moving vessel. Simulation analysis was carried out on the tracking effect of the traditional Kalman filter and improved Kalman filter. The results indicate that the proposed algorithm can solve the lack in information of AIS equipment, and accurately predict the location of a vessel. The accuracy and the reliability of intelligence supporting command system can be ensured in controlled waterway.

It is difficult to effectually track and monitor a vessel by video monitoring in narrow and winding controlled areas of Yangtze River. In this situation, directly using the common method for maritime video surveillance would result in tracking lag, alignment error, and even loss of target. A method of combining Automatic Identification System (AIS) and multiple Pan-Tilt-Zoom (PTZ) cameras was proposed to trace and monitor the vessel in controlled areas of Yangtze River. The latitude and longitude of the target vessel were utilized to initialize the alignment of PTZ cameras. Meanwhile, vessel’s speed and heading were applied for dynamic prediction tracking to smoothen image and eliminate tracking lag, while image recognition was employed to correct error for ensuring that target vessel was located in the optimum surveillance area.The simulation results indicate that this algorithm can not only track target vessel continuously and dynamically, but also realize the real-time target tracking with high precision.