Multisensor Tracking of Marine Targets - Decentralized Fusion of Kalman and Neural Filters
Multisensor Tracking of Marine Targets - Decentralized Fusion of Kalman and Neural Filters
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DOI：
10.2478/v10177-011-0009-8
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发表时间：
2011-03-01
影响因子：
0.7
通讯作者：
Kazimierski, Witold
中科院分区：
文献类型：
--
作者：
Stateczny, Andrzej;Kazimierski, Witold
本文提出了一种多传感器分布式数据融合的雷达海上目标跟踪算法。融合是在卡尔曼滤波器的空间中进行的，并且通过找到由每个传感器提供的单个状态估计的加权平均来完成。传感器使用数字或神经滤波器进行跟踪。本文介绍了两种跟踪方法--卡尔曼滤波和广义回归神经网络，以及融合算法。确定了运动目标的结构模型和测量模型。数据融合的两种方法-集中式和分散式-后者进行了彻底的检查。在此基础上，讨论了复杂雷达系统中的主要引信处理问题。这包括坐标转换、航迹关联和测量同步。最后给出了仿真跟踪和融合过程的数值实验结果。文章最后总结了研究过程中指出的问题。
This paper presents an algorithm of multisensor decentralized data fusion for radar tracking of maritime targets. The fusion is performed in the space of Kalman Filter and is done by finding weighted average of single state estimates provided be each of the sensors. The sensors use numerical or neural filters for tracking. The article presents two tracking methods - Kalman Filter and General Regression Neural Network, together with the fusion algorithm. The structural and measurement models of moving target are determined. Two approaches for data fusion are stated - centralized and decentralized - and the latter is thoroughly examined. Further, the discussion on main fusing process problems in complex radar systems is presented. This includes coordinates transformation, track association and measurements synchronization. The results of numerical experiment simulating tracking and fusion process are highlighted. The article is ended with a summary of the issues pointed out during the research.