基于改进卡尔曼滤波的控制河段船舶航迹预测

由于船舶自动识别系统(AIS)设备存在信息缺失现象，导致基于AIS的智能辅助指挥系统无法准确判断船舶位置，难以准确揭示通行信号。同时，控制河段具有航道狭窄弯曲等特征，传统卡尔曼滤波算法无法准确预测运动船舶的航迹。针对以上问题，对卡尔曼滤波算法中的系统噪声进行实时估计，以提高船舶航迹的预测精度，并对传统卡尔曼滤波和改进卡尔曼滤波的跟踪效果进行了仿真分析。结果表明，所提算法可有效解决AIS设备信息缺失问题，准确预测船舶位置，保证控制河段智能辅助指挥系统信号揭示的准确性和可靠性。

Track prediction of vessel in controlled waterway based on improved Kalman filter

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.