卡尔曼算法在船舶运动模型参数辨别中的应用

Application of Kalman algorithm in the ship motion model parameters identify

人工智能技术飞速发展,人们已经实现了依靠智能算法识别船舶运动轨迹,这对船舶的航行安全起着重要的推进作用。在对船舶运动状态进行跟踪识别过程中,首先要从视频图像中提取出目标物体的特征值,然后在数据库中进行比对,进一步获得目标物的具体参数。本文采用卡尔曼算法对船舶运动目标的特征参数进行分析,首先建立船舶运动时的动力模型,然后结合目标的灰度统计特性,对船舶的运动状态和运动参数的转移算法进行设计。从仿真结果可知,本文所提出的运动模型识别算法满足基本的鲁棒性和准确度,能够对船舶目标进行速度识别和航迹跟踪。

With the rapid development of artificial intelligence, people have realized rely on intelligent algorithms to identify the ship trajectory,safety of navigation of the ship which plays an important role in the promotion. In the process of tracking the movement of the vessels identified in the first extracted from the video image characteristic value of the target object,and then compare the database to further obtain the specific parameters of the target object. In this paper,Kalman algorithm parameters of a ship moving target analysis,the article first established a dynamic model of the ship' s movement,and then combined with the gray of the statistical characteristics of the target for transfer algorithm ship motion and motion parameters of the design. From the simulation results,the movement pattern recognition algorithm proposed in this paper to meet the basic robustness and accuracy,it is possible to identify the ship target speed and trajectory tracking.